2 types of research problem

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What is a Research Problem? Characteristics, Types, and Examples

A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation. The statement of a problem orients the reader to the importance of the topic, sets the problem into a particular context, and defines the relevant parameters, providing the framework for reporting the findings. Therein lies the importance of research problem s.  

The formulation of well-defined research questions is central to addressing a research problem . A research question is a statement made in a question form to provide focus, clarity, and structure to the research endeavor. This helps the researcher design methodologies, collect data, and analyze results in a systematic and coherent manner. A study may have one or more research questions depending on the nature of the study.   

Identifying and addressing a research problem is very important. By starting with a pertinent problem , a scholar can contribute to the accumulation of evidence-based insights, solutions, and scientific progress, thereby advancing the frontier of research. Moreover, the process of formulating research problems and posing pertinent research questions cultivates critical thinking and hones problem-solving skills.   

Table of Contents

What is a Research Problem ?  

Before you conceive of your project, you need to ask yourself “ What is a research problem ?” A research problem definition can be broadly put forward as the primary statement of a knowledge gap or a fundamental challenge in a field, which forms the foundation for research. Conversely, the findings from a research investigation provide solutions to the problem .  

A research problem guides the selection of approaches and methodologies, data collection, and interpretation of results to find answers or solutions. A well-defined problem determines the generation of valuable insights and contributions to the broader intellectual discourse.  

Characteristics of a Research Problem  

Knowing the characteristics of a research problem is instrumental in formulating a research inquiry; take a look at the five key characteristics below:  

Novel : An ideal research problem introduces a fresh perspective, offering something new to the existing body of knowledge. It should contribute original insights and address unresolved matters or essential knowledge.   

Significant : A problem should hold significance in terms of its potential impact on theory, practice, policy, or the understanding of a particular phenomenon. It should be relevant to the field of study, addressing a gap in knowledge, a practical concern, or a theoretical dilemma that holds significance.  

Feasible: A practical research problem allows for the formulation of hypotheses and the design of research methodologies. A feasible research problem is one that can realistically be investigated given the available resources, time, and expertise. It should not be too broad or too narrow to explore effectively, and should be measurable in terms of its variables and outcomes. It should be amenable to investigation through empirical research methods, such as data collection and analysis, to arrive at meaningful conclusions A practical research problem considers budgetary and time constraints, as well as limitations of the problem . These limitations may arise due to constraints in methodology, resources, or the complexity of the problem.  

Clear and specific : A well-defined research problem is clear and specific, leaving no room for ambiguity; it should be easily understandable and precisely articulated. Ensuring specificity in the problem ensures that it is focused, addresses a distinct aspect of the broader topic and is not vague.  

Rooted in evidence: A good research problem leans on trustworthy evidence and data, while dismissing unverifiable information. It must also consider ethical guidelines, ensuring the well-being and rights of any individuals or groups involved in the study.

Types of Research Problems  

Across fields and disciplines, there are different types of research problems . We can broadly categorize them into three types.  

• Theoretical research problems

Theoretical research problems deal with conceptual and intellectual inquiries that may not involve empirical data collection but instead seek to advance our understanding of complex concepts, theories, and phenomena within their respective disciplines. For example, in the social sciences, research problem s may be casuist (relating to the determination of right and wrong in questions of conduct or conscience), difference (comparing or contrasting two or more phenomena), descriptive (aims to describe a situation or state), or relational (investigating characteristics that are related in some way).  

Here are some theoretical research problem examples :   

• Ethical frameworks that can provide coherent justifications for artificial intelligence and machine learning algorithms, especially in contexts involving autonomous decision-making and moral agency.  
• Determining how mathematical models can elucidate the gradual development of complex traits, such as intricate anatomical structures or elaborate behaviors, through successive generations.  
• Applied research problems

Applied or practical research problems focus on addressing real-world challenges and generating practical solutions to improve various aspects of society, technology, health, and the environment.  

Here are some applied research problem examples :   

• Studying the use of precision agriculture techniques to optimize crop yield and minimize resource waste.  
• Designing a more energy-efficient and sustainable transportation system for a city to reduce carbon emissions.  
• Action research problems

Action research problems aim to create positive change within specific contexts by involving stakeholders, implementing interventions, and evaluating outcomes in a collaborative manner.  

Here are some action research problem examples :   

• Partnering with healthcare professionals to identify barriers to patient adherence to medication regimens and devising interventions to address them.  
• Collaborating with a nonprofit organization to evaluate the effectiveness of their programs aimed at providing job training for underserved populations.  

These different types of research problems may give you some ideas when you plan on developing your own.  

How to Define a Research Problem  

You might now ask “ How to define a research problem ?” These are the general steps to follow:   

• Look for a broad problem area: Identify under-explored aspects or areas of concern, or a controversy in your topic of interest. Evaluate the significance of addressing the problem in terms of its potential contribution to the field, practical applications, or theoretical insights.
• Learn more about the problem: Read the literature, starting from historical aspects to the current status and latest updates. Rely on reputable evidence and data. Be sure to consult researchers who work in the relevant field, mentors, and peers. Do not ignore the gray literature on the subject.
• Identify the relevant variables and how they are related: Consider which variables are most important to the study and will help answer the research question. Once this is done, you will need to determine the relationships between these variables and how these relationships affect the research problem . 
• Think of practical aspects : Deliberate on ways that your study can be practical and feasible in terms of time and resources. Discuss practical aspects with researchers in the field and be open to revising the problem based on feedback. Refine the scope of the research problem to make it manageable and specific; consider the resources available, time constraints, and feasibility.
• Formulate the problem statement: Craft a concise problem statement that outlines the specific issue, its relevance, and why it needs further investigation.
• Stick to plans, but be flexible: When defining the problem , plan ahead but adhere to your budget and timeline. At the same time, consider all possibilities and ensure that the problem and question can be modified if needed.

Key Takeaways  

• A research problem concerns an area of interest, a situation necessitating improvement, an obstacle requiring eradication, or a challenge in theory or practical applications.   
• The importance of research problem is that it guides the research and helps advance human understanding and the development of practical solutions.  
• Research problem definition begins with identifying a broad problem area, followed by learning more about the problem, identifying the variables and how they are related, considering practical aspects, and finally developing the problem statement.  
• Different types of research problems include theoretical, applied, and action research problems , and these depend on the discipline and nature of the study.  
• An ideal problem is original, important, feasible, specific, and based on evidence.  

Frequently Asked Questions  

Why is it important to define a research problem?  

Identifying potential issues and gaps as research problems is important for choosing a relevant topic and for determining a well-defined course of one’s research. Pinpointing a problem and formulating research questions can help researchers build their critical thinking, curiosity, and problem-solving abilities.   

How do I identify a research problem?  

Identifying a research problem involves recognizing gaps in existing knowledge, exploring areas of uncertainty, and assessing the significance of addressing these gaps within a specific field of study. This process often involves thorough literature review, discussions with experts, and considering practical implications.  

Can a research problem change during the research process?  

Yes, a research problem can change during the research process. During the course of an investigation a researcher might discover new perspectives, complexities, or insights that prompt a reevaluation of the initial problem. The scope of the problem, unforeseen or unexpected issues, or other limitations might prompt some tweaks. You should be able to adjust the problem to ensure that the study remains relevant and aligned with the evolving understanding of the subject matter.

How does a research problem relate to research questions or hypotheses?  

A research problem sets the stage for the study. Next, research questions refine the direction of investigation by breaking down the broader research problem into manageable components. Research questions are formulated based on the problem , guiding the investigation’s scope and objectives. The hypothesis provides a testable statement to validate or refute within the research process. All three elements are interconnected and work together to guide the research.  

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Home » Research Problem – Examples, Types and Guide

Research Problem – Examples, Types and Guide

Table of Contents

Research Problem

Definition:

Research problem is a specific and well-defined issue or question that a researcher seeks to investigate through research. It is the starting point of any research project, as it sets the direction, scope, and purpose of the study.

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

These problems involve describing or documenting a particular phenomenon, event, or situation. For example, a researcher might investigate the demographics of a particular population, such as their age, gender, income, and education.

Exploratory problems

These problems are designed to explore a particular topic or issue in depth, often with the goal of generating new ideas or hypotheses. For example, a researcher might explore the factors that contribute to job satisfaction among employees in a particular industry.

Explanatory Problems

These problems seek to explain why a particular phenomenon or event occurs, and they typically involve testing hypotheses or theories. For example, a researcher might investigate the relationship between exercise and mental health, with the goal of determining whether exercise has a causal effect on mental health.

Predictive Problems

These problems involve making predictions or forecasts about future events or trends. For example, a researcher might investigate the factors that predict future success in a particular field or industry.

Evaluative Problems

These problems involve assessing the effectiveness of a particular intervention, program, or policy. For example, a researcher might evaluate the impact of a new teaching method on student learning outcomes.

How to Define a Research Problem

Defining a research problem involves identifying a specific question or issue that a researcher seeks to address through a research study. Here are the steps to follow when defining a research problem:

• Identify a broad research topic : Start by identifying a broad topic that you are interested in researching. This could be based on your personal interests, observations, or gaps in the existing literature.
• Conduct a literature review : Once you have identified a broad topic, conduct a thorough literature review to identify the current state of knowledge in the field. This will help you identify gaps or inconsistencies in the existing research that can be addressed through your study.
• Refine the research question: Based on the gaps or inconsistencies identified in the literature review, refine your research question to a specific, clear, and well-defined problem statement. Your research question should be feasible, relevant, and important to the field of study.
• Develop a hypothesis: Based on the research question, develop a hypothesis that states the expected relationship between variables.
• Define the scope and limitations: Clearly define the scope and limitations of your research problem. This will help you focus your study and ensure that your research objectives are achievable.
• Get feedback: Get feedback from your advisor or colleagues to ensure that your research problem is clear, feasible, and relevant to the field of study.

Components of a Research Problem

The components of a research problem typically include the following:

• Topic : The general subject or area of interest that the research will explore.
• Research Question : A clear and specific question that the research seeks to answer or investigate.
• Objective : A statement that describes the purpose of the research, what it aims to achieve, and the expected outcomes.
• Hypothesis : An educated guess or prediction about the relationship between variables, which is tested during the research.
• Variables : The factors or elements that are being studied, measured, or manipulated in the research.
• Methodology : The overall approach and methods that will be used to conduct the research.
• Scope and Limitations : A description of the boundaries and parameters of the research, including what will be included and excluded, and any potential constraints or limitations.
• Significance: A statement that explains the potential value or impact of the research, its contribution to the field of study, and how it will add to the existing knowledge.

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

• Exploring the impact of social media on adolescent mental health.
• Investigating the effectiveness of cognitive-behavioral therapy for treating anxiety disorders.
• Studying the impact of prenatal stress on child development outcomes.
• Analyzing the factors that contribute to addiction and relapse in substance abuse treatment.
• Examining the impact of personality traits on romantic relationships.

Research Problem Examples in Sociology are as follows:

• Investigating the relationship between social support and mental health outcomes in marginalized communities.
• Studying the impact of globalization on labor markets and employment opportunities.
• Analyzing the causes and consequences of gentrification in urban neighborhoods.
• Investigating the impact of family structure on social mobility and economic outcomes.
• Examining the effects of social capital on community development and resilience.

Research Problem Examples in Economics are as follows:

• Studying the effects of trade policies on economic growth and development.
• Analyzing the impact of automation and artificial intelligence on labor markets and employment opportunities.
• Investigating the factors that contribute to economic inequality and poverty.
• Examining the impact of fiscal and monetary policies on inflation and economic stability.
• Studying the relationship between education and economic outcomes, such as income and employment.

Political Science

Research Problem Examples in Political Science are as follows:

• Analyzing the causes and consequences of political polarization and partisan behavior.
• Investigating the impact of social movements on political change and policymaking.
• Studying the role of media and communication in shaping public opinion and political discourse.
• Examining the effectiveness of electoral systems in promoting democratic governance and representation.
• Investigating the impact of international organizations and agreements on global governance and security.

Environmental Science

Research Problem Examples in Environmental Science are as follows:

• Studying the impact of air pollution on human health and well-being.
• Investigating the effects of deforestation on climate change and biodiversity loss.
• Analyzing the impact of ocean acidification on marine ecosystems and food webs.
• Studying the relationship between urban development and ecological resilience.
• Examining the effectiveness of environmental policies and regulations in promoting sustainability and conservation.

Research Problem Examples in Education are as follows:

• Investigating the impact of teacher training and professional development on student learning outcomes.
• Studying the effectiveness of technology-enhanced learning in promoting student engagement and achievement.
• Analyzing the factors that contribute to achievement gaps and educational inequality.
• Examining the impact of parental involvement on student motivation and achievement.
• Studying the effectiveness of alternative educational models, such as homeschooling and online learning.

Research Problem Examples in History are as follows:

• Analyzing the social and economic factors that contributed to the rise and fall of ancient civilizations.
• Investigating the impact of colonialism on indigenous societies and cultures.
• Studying the role of religion in shaping political and social movements throughout history.
• Analyzing the impact of the Industrial Revolution on economic and social structures.
• Examining the causes and consequences of global conflicts, such as World War I and II.

Research Problem Examples in Business are as follows:

• Studying the impact of corporate social responsibility on brand reputation and consumer behavior.
• Investigating the effectiveness of leadership development programs in improving organizational performance and employee satisfaction.
• Analyzing the factors that contribute to successful entrepreneurship and small business development.
• Examining the impact of mergers and acquisitions on market competition and consumer welfare.
• Studying the effectiveness of marketing strategies and advertising campaigns in promoting brand awareness and sales.

Research Problem Example for Students

An Example of a Research Problem for Students could be:

“How does social media usage affect the academic performance of high school students?”

This research problem is specific, measurable, and relevant. It is specific because it focuses on a particular area of interest, which is the impact of social media on academic performance. It is measurable because the researcher can collect data on social media usage and academic performance to evaluate the relationship between the two variables. It is relevant because it addresses a current and important issue that affects high school students.

To conduct research on this problem, the researcher could use various methods, such as surveys, interviews, and statistical analysis of academic records. The results of the study could provide insights into the relationship between social media usage and academic performance, which could help educators and parents develop effective strategies for managing social media use among students.

Another example of a research problem for students:

“Does participation in extracurricular activities impact the academic performance of middle school students?”

This research problem is also specific, measurable, and relevant. It is specific because it focuses on a particular type of activity, extracurricular activities, and its impact on academic performance. It is measurable because the researcher can collect data on students’ participation in extracurricular activities and their academic performance to evaluate the relationship between the two variables. It is relevant because extracurricular activities are an essential part of the middle school experience, and their impact on academic performance is a topic of interest to educators and parents.

To conduct research on this problem, the researcher could use surveys, interviews, and academic records analysis. The results of the study could provide insights into the relationship between extracurricular activities and academic performance, which could help educators and parents make informed decisions about the types of activities that are most beneficial for middle school students.

Applications of Research Problem

Applications of Research Problem are as follows:

• Academic research: Research problems are used to guide academic research in various fields, including social sciences, natural sciences, humanities, and engineering. Researchers use research problems to identify gaps in knowledge, address theoretical or practical problems, and explore new areas of study.
• Business research : Research problems are used to guide business research, including market research, consumer behavior research, and organizational research. Researchers use research problems to identify business challenges, explore opportunities, and develop strategies for business growth and success.
• Healthcare research : Research problems are used to guide healthcare research, including medical research, clinical research, and health services research. Researchers use research problems to identify healthcare challenges, develop new treatments and interventions, and improve healthcare delivery and outcomes.
• Public policy research : Research problems are used to guide public policy research, including policy analysis, program evaluation, and policy development. Researchers use research problems to identify social issues, assess the effectiveness of existing policies and programs, and develop new policies and programs to address societal challenges.
• Environmental research : Research problems are used to guide environmental research, including environmental science, ecology, and environmental management. Researchers use research problems to identify environmental challenges, assess the impact of human activities on the environment, and develop sustainable solutions to protect the environment.

Purpose of Research Problems

The purpose of research problems is to identify an area of study that requires further investigation and to formulate a clear, concise and specific research question. A research problem defines the specific issue or problem that needs to be addressed and serves as the foundation for the research project.

Identifying a research problem is important because it helps to establish the direction of the research and sets the stage for the research design, methods, and analysis. It also ensures that the research is relevant and contributes to the existing body of knowledge in the field.

A well-formulated research problem should:

• Clearly define the specific issue or problem that needs to be investigated
• Be specific and narrow enough to be manageable in terms of time, resources, and scope
• Be relevant to the field of study and contribute to the existing body of knowledge
• Be feasible and realistic in terms of available data, resources, and research methods
• Be interesting and intellectually stimulating for the researcher and potential readers or audiences.

Characteristics of Research Problem

The characteristics of a research problem refer to the specific features that a problem must possess to qualify as a suitable research topic. Some of the key characteristics of a research problem are:

• Clarity : A research problem should be clearly defined and stated in a way that it is easily understood by the researcher and other readers. The problem should be specific, unambiguous, and easy to comprehend.
• Relevance : A research problem should be relevant to the field of study, and it should contribute to the existing body of knowledge. The problem should address a gap in knowledge, a theoretical or practical problem, or a real-world issue that requires further investigation.
• Feasibility : A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources.
• Novelty : A research problem should be novel or original in some way. It should represent a new or innovative perspective on an existing problem, or it should explore a new area of study or apply an existing theory to a new context.
• Importance : A research problem should be important or significant in terms of its potential impact on the field or society. It should have the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Manageability : A research problem should be manageable in terms of its scope and complexity. It should be specific enough to be investigated within the available time and resources, and it should be broad enough to provide meaningful results.

Advantages of Research Problem

The advantages of a well-defined research problem are as follows:

• Focus : A research problem provides a clear and focused direction for the research study. It ensures that the study stays on track and does not deviate from the research question.
• Clarity : A research problem provides clarity and specificity to the research question. It ensures that the research is not too broad or too narrow and that the research objectives are clearly defined.
• Relevance : A research problem ensures that the research study is relevant to the field of study and contributes to the existing body of knowledge. It addresses gaps in knowledge, theoretical or practical problems, or real-world issues that require further investigation.
• Feasibility : A research problem ensures that the research study is feasible in terms of the availability of data, resources, and research methods. It ensures that the research is realistic and practical to conduct within the available time, budget, and resources.
• Novelty : A research problem ensures that the research study is original and innovative. It represents a new or unique perspective on an existing problem, explores a new area of study, or applies an existing theory to a new context.
• Importance : A research problem ensures that the research study is important and significant in terms of its potential impact on the field or society. It has the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Rigor : A research problem ensures that the research study is rigorous and follows established research methods and practices. It ensures that the research is conducted in a systematic, objective, and unbiased manner.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Different types of research problems and their examples

The identification of the research problem is the first step in the research process. It is similar to the identification of the destination before a journey. It works as the foundation for the whole research process . In the field of social sciences, a research problem is presented in the form of a question. It helps in narrowing down the issue to something reasonable for conducting a study. Defining a research problem serves three main purposes (Pardede, 2018):

• It presents the importance of the research topic.
• It helps the researcher place the problem in a specific context to properly define the parameters of the investigation.
• It provides a framework that can help in presenting the results in the future.

In absolute terms, a research problem can be defined as a statement regarding the area of concern, a condition that needs to be improved, an unresolved question that exists in the literature, a difficulty that needs to be eliminated or any point that needs some meaningful investigation (Gallupe, 2007).

To ideally conclude the research, find logical answers to your research problems.

Descriptive research problems

Descriptive research problems focus on questions like ‘what is ?’, with its main aim to describe the situation, state or the existence of certain specific phenomena. They seek to depict what already exists in a group or population. For such studies, surveys and opinion polls are best suitable because they require systematic observation of social issues.

What are the main factors affecting consumers’ purchase decisions?

These problems use two different ways to collect data- cross-sectional studies and longitudinal studies. Cross-sectional studies provide a snapshot of data at a certain moment in time. On the other hand, longitudinal studies involve a fixed and stable sample that is measured repeatedly over time. However, in both cases, methods that can be used to collect data include mail, online or offline surveys, and interviews. When a researcher is dealing with a descriptive research problem, there can be no manipulation in the variables and hypotheses as they are usually nondirectional (Hashimi, 2015).

Causal research problems

Causal research problems focus on identifying the extent and nature of cause-and-effect relationships. Such research problems help in assessing the impact of some changes on existing norms and processes. They thus identify patterns of relationships between different elements.

How does online education affect students’ learning abilities?

In such cases, experiments are the most popular way of collecting primary data. Here, the hypothesis is usually directional, i.e. explaining how one factor affects the behaviour of another one. Such studies give the researcher the freedom to manipulate the variables as desired. Data for causal research can be collected in two ways:

• laboratory experiments and,
• field experiments.

Laboratory experiments are generally conducted in an artificial environment which allows the researcher to carefully manipulate the variables. On the other hand, field experiments are much more realistic. It is sometimes not possible to control the variables. This makes it difficult for the researcher to predict with confidence what produced a given outcome (Muhammad and Kabir, 2018).

Relational research problem

This research problem states that some sort of relationship between two variables needs to be investigated. The aim is to investigate the qualities or characteristics that are connected in some way.

How is the teaching experience of a teacher with respect to their teaching style?

Thus, this sort of research problem requires more than one variable that describes the relationship between them (Hartanska, 2014).

Summarizing the differences

How to choose the right research problem type?

While choosing the research problem type one must keep in mind the following points.

• The first step in direction of selecting the right problem type is to identify the concepts and terms that make up the topic. This involves identifying the variables of the study. For example, if there is only one variable then it is a descriptive research problem. If it contains two variables, then it is likely relational or causal research.
• The second step is to review the literature to refine the approach of examining the topic and finding the appropriate ways to analyze it. For example, how much research has already been conducted on this topic? What methods and data did the previous researchers use? What was lacking in their research? What variables were used by them? The answers to these questions will help in framing the best approach to your research.
• The third step is to look for sources that can help broaden, modify and strengthen your initial thoughts. A deeper look into the research will answer critical questions like, is a relational approach better than an investigative one? How will eliminating a few variables affect the outcome of the research?
• Gallupe, R. B. (2007) ‘Research contributions: The tyranny of methodologies in information systems research, ACM SIGMIS Database , 38(3), pp. 46–57.
• Hartanska, J. (2014) ‘THE RESEARCH PROBLEM’, pp. 1–48.
• Hashimi, H. (2015) ‘Types of research questions’, Nursing , 4(3), pp. 23–25.
• Muhammad, S. and Kabir, S. (2018) ‘Problem formulation and objective determination’, (June).
• Pardede, P. (2018) ‘Identifying and Formulating the Research Problem’, Research in ELT , 1(October), pp. 1–13.
• Priya Chetty
• Ashni walia

I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them. 

Over the past decade I have also built a profile as a researcher on Project Guru's Knowledge Tank division. I have penned over 200 articles that have earned me 400+ citations so far. My Google Scholar profile can be accessed here . 

I now consult university faculty through Faculty Development Programs (FDPs) on the latest developments in the field of research. I also guide individual researchers on how they can commercialise their inventions or research findings. Other developments im actively involved in at Project Guru include strengthening the "Publish" division as a bridge between industry and academia by bringing together experienced research persons, learners, and practitioners to collaboratively work on a common goal. 

I am a master's in Economics from Amity university. Besides my keen interest in Economics i have been an active member of the team Enactus. Apart from the academics i love reading fictions. 

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

The term rationale of research means the reason for performing the research study in question.

In this post you’ll learn what the significance of the study means, why it’s important, where and how to write one in your paper or thesis with an example.

An abstract and introduction are the first two sections of your paper or thesis. This guide explains the differences between them and how to write them.

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The scope and delimitations of a thesis, dissertation or paper define the topic and boundaries of a research problem – learn how to form them.

This post explains where and how to write the list of figures in your thesis or dissertation.

Kamal is a second year PhD student University of Toronto in the department of Chemistry. His research is focused on making hydrogen gas more affordable and easier to generate from water to use as a clean energy source.

Annabel is a third-year PhD student at the University of Glasgow, looking at the effects of online self-diagnosis and health information seeking on the patient-healthcare professional relationship.

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The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

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Research Problem – Definition, Steps & Tips

Published by Jamie Walker at August 12th, 2021 , Revised On October 3, 2023

Once you have chosen a research topic, the next stage is to explain the research problem: the detailed issue, ambiguity of the research, gap analysis, or gaps in knowledge and findings that you will discuss.

Here, in this article, we explore a research problem in a dissertation or an essay with some research problem examples to help you better understand how and when you should write a research problem.

“A research problem is a specific statement relating to an area of concern and is contingent on the type of research. Some research studies focus on theoretical and practical problems, while some focus on only one.”

The problem statement in the dissertation, essay, research paper, and other academic papers should be clearly stated and intended to expand information, knowledge, and contribution to change.

This article will assist in identifying and elaborating a research problem if you are unsure how to define your research problem. The most notable challenge in the research process is to formulate and identify a research problem. Formulating a problem statement and research questions while finalizing the research proposal or introduction for your dissertation or thesis is necessary.

Why is Research Problem Critical?

An interesting research topic is only the first step. The real challenge of the research process is to develop a well-rounded research problem.

A well-formulated research problem helps understand the research procedure; without it, your research will appear unforeseeable and awkward.

Research is a procedure based on a sequence and a research problem aids in following and completing the research in a sequence. Repetition of existing literature is something that should be avoided in research.

Therefore research problem in a dissertation or an essay needs to be well thought out and presented with a clear purpose. Hence, your research work contributes more value to existing knowledge. You need to be well aware of the problem so you can present logical solutions.

Formulating a research problem is the first step of conducting research, whether you are writing an essay, research paper,   dissertation , or  research proposal .

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Step 1: Identifying Problem Area – What is Research Problem

The most significant step in any research is to look for  unexplored areas, topics, and controversies . You aim to find gaps that your work will fill. Here are some research problem examples for you to better understand the concept.

Practical Research Problems

To conduct practical research, you will need practical research problems that are typically identified by analysing reports, previous research studies, and interactions with the experienced personals of pertinent disciplines. You might search for:

• Problems with performance or competence in an organization
• Institutional practices that could be enhanced
• Practitioners of relevant fields and their areas of concern
• Problems confronted by specific groups of people within your area of study

If your research work relates to an internship or a job, then it will be critical for you to identify a research problem that addresses certain issues faced by the firm the job or internship pertains to.

Examples of Practical Research Problems

Decreased voter participation in county A, as compared to the rest of the country.

The high employee turnover rate of department X of company Y influenced efficiency and team performance.

A charity institution, Y, suffers a lack of funding resulting in budget cuts for its programmes.

Theoretical Research Problems

Theoretical research relates to predicting, explaining, and understanding various phenomena. It also expands and challenges existing information and knowledge.

Identification of a research problem in theoretical research is achieved by analysing theories and fresh research literature relating to a broad area of research. This practice helps to find gaps in the research done by others and endorse the argument of your topic.

Here are some questions that you should bear in mind.

• A case or framework that has not been deeply analysed
• An ambiguity between more than one viewpoints
• An unstudied condition or relationships
• A problematic issue that needs to be addressed

Theoretical issues often contain practical implications, but immediate issues are often not resolved by these results. If that is the case, you might want to adopt a different research approach  to achieve the desired outcomes.

Examples of Theoretical Research Problems

Long-term Vitamin D deficiency affects cardiac patients are not well researched.

The relationship between races, sex, and income imbalances needs to be studied with reference to the economy of a specific country or region.

The disagreement among historians of Scottish nationalism regarding the contributions of Imperial Britain in the creation of the national identity for Scotland.

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Step 2: Understanding the Research Problem

The researcher further investigates the selected area of research to find knowledge and information relating to the research problem to address the findings in the research.

Background and Rationale

• Population influenced by the problem?
• Is it a persistent problem, or is it recently revealed?
• Research that has already been conducted on this problem?
• Any proposed solution to the problem?
• Recent arguments concerning the problem, what are the gaps in the problem?

How to Write a First Class Dissertation Proposal or Research Proposal

Particularity and Suitability

• What specific place, time, and/or people will be focused on?
• Any aspects of research that you may not be able to deal with?
• What will be the concerns if the problem remains unresolved?
• What are the benefices of the problem resolution (e.g. future researcher or organisation’s management)?

Example of a Specific Research Problem

A non-profit institution X has been examined on their existing support base retention, but the existing research does not incorporate an understanding of how to effectively target new donors. To continue their work, the institution needs more research and find strategies for effective fundraising.

Once the problem is narrowed down, the next stage is to propose a problem statement and hypothesis or research questions.

If you are unsure about what a research problem is and how to define the research problem, then you might want to take advantage of our dissertation proposal writing service. You may also want to take a look at our essay writing service if you need help with identifying a research problem for your essay.

Frequently Asked Questions

What is research problem with example.

A research problem is a specific challenge that requires investigation. Example: “What is the impact of social media on mental health among adolescents?” This problem drives research to analyse the relationship between social media use and mental well-being in young people.

How many types of research problems do we have?

• Descriptive: Describing phenomena as they exist.
• Explanatory: Understanding causes and effects.
• Exploratory: Investigating little-understood phenomena.
• Predictive: Forecasting future outcomes.
• Prescriptive: Recommending actions.
• Normative: Describing what ought to be.

What are the principles of the research problem?

• Relevance: Addresses a significant issue.
• Re searchability: Amenable to empirical investigation.
• Clarity: Clearly defined without ambiguity.
• Specificity: Narrowly framed, avoiding vagueness.
• Feasibility: Realistic to conduct with available resources.
• Novelty: Offers new insights or challenges existing knowledge.
• Ethical considerations: Respect rights, dignity, and safety.

Why is research problem important?

A research problem is crucial because it identifies knowledge gaps, directs the inquiry’s focus, and forms the foundation for generating hypotheses or questions. It drives the methodology and determination of study relevance, ensuring that research contributes meaningfully to academic discourse and potentially addresses real-world challenges.

How do you write a research problem?

To write a research problem, identify a knowledge gap or an unresolved issue in your field. Start with a broad topic, then narrow it down. Clearly articulate the problem in a concise statement, ensuring it’s researchable, significant, and relevant. Ground it in the existing literature to highlight its importance and context.

How can we solve research problem?

To solve a research problem, start by conducting a thorough literature review. Formulate hypotheses or research questions. Choose an appropriate research methodology. Collect and analyse data systematically. Interpret findings in the context of existing knowledge. Ensure validity and reliability, and discuss implications, limitations, and potential future research directions.

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5 Different Sources of a Research Problem And Their Significance

by  Antony W

September 2, 2024

In this guide, you will learn about the best sources of a research problem for your next project. 

What is a Research Problem?

The term research problem refers to a clear expression of an area of concern that requires a clear understanding and deliberate investigation.

While it offers a broad proposition and a valuable question, a research problem doesn’t demonstrate how to do something.

It’s worth looking at a research problem for a number of reasons. It introduces a reader to the topic under investigation and orients to the importance of the study.

Why is a Research Problem Important? 

Besides allowing you to define the most important parameter to investigate in your paper, a research problem offers you a concise guide to come up with research questions , make relevant assumptions, and formulate a proposition. 

More importantly, a research problem gives you a more comprehensive framework to conduct extensive studies and explain your findings.

Types of Research Problems

The different types of research problems are casuist, difference, descriptive and relational research problems.

1. Relational Research Problem

A relational research problem suggests the need to investigate the correlation between two or more variables.

It’s the researcher’s responsibility to investigate a number of precise characteristics and identify the relationship between them.

2. Casuist Research Problem

Casuist research problem has something to do with the determination of what’s right and what’s wrong.

It questions human conduct by looking closely at the moral dilemmas by means of careful differentiation of cases as well as the application of general rules.

3. Descriptive Research Problem

In this case, a researcher looks forward to investigating a “what is” kind of issue.

The goal of examining a descriptive research problem is to determine the underlying significance of an event or the existence of a situation.

It’s with the descriptive research problem that a researcher can discover understudied or hidden issues.

5. Difference Research Problem

A difference research problem focuses on the distinction between two or more groups.  More often than not, researchers use this type of problem to compare and contrast more than one phenomenon.

What are the Sources of Research Problems?

The sources that you can use to identify research problems are interviews, personal experiences, deductions from theory, interdisciplinary perspective, and relevant literature. 

From a research perspective, the kind of research problem that you wish to investigate should meet two conditions.

• The problem has to be unique and not something other researchers have already looked into exhaustively.
• The problem has to be concise enough to raise specific issues that you can address in a research paper .

1. Interviews

Interviews sessions can be significant sources of research problems. The method gives you an opportunity to have formal discussions and informal interactions with individuals who can provide useful insights into research and make findings more relevant to future research. 

Consider having discussions with experts in the field you wish to investigate. These professionals mat be healthcare service providers, business leaders, teachers, social workers, attorneys, and accountants to mention but a few examples.

By interacting with these experts, you’re able to identify real-world problems that researchers have either ignored or understudied in the academic space.

Moreover, interview sessions give you the opportunity to get some practical knowledge that can help you to design and conduct your studies.

2. Personal Experiences

Your everyday experiences are a good source of research problem.

You have to think critically about your personal experiences with an issue that affects your family, your personal life, or your community.

A research problem derived from personal experience can spring from any issue and from anywhere.

For example, you can construct a research problem from events that appear to be out of the ordinary or from community relationships that don’t have clear explanations.

3. Deductions from Theory

A deduction from theory refers to inferences a researcher makes from the generalizations of life in a society that a researcher knows very well.

A researcher takes the deduction, places them in an empirical frame, and then, based on a theory, they come up with a research problem and a hypothesis that suggests some findings based on given empirical results.

The research accounts for the relationship to observe if a theory summarizes the state of an affair.

A systematic investigation, which evaluates if the empirical information affirms or rejects the hypothesis , comes next.

4. Interdisciplinary Perspective

If you consider interdisciplinary perspective to identify a problem for a research study, you’ll have to look at scholarship and academic movements from outside your main area of investigation.

It’s an intellectually involving process, one that requires reviewing pertinent literature to discover unique avenues of exploration an analysis.

The benefit of using this approach to identify a research problem for your research paper assignment is that it presents an opportunity for you to understand complex issues with ease.

5. Relevant Literature

To generate a research problem from relevant literature, you first have to review research related to your area of interest.

Doing so allows you to find gaps on the topic, making it easy for you to understand just how much understudied your area of interest is.

Data collected from relevant literature is relevant because it helps to:

• Fill existing gaps in knowledge based on a specific research
• Determine if current studies can have implications on further research on the same issue
• See if it’s possible to conduct a similar study in a different area or apply the same in a different context
• Determine if the methods used in previous studies can be effective in solving future problems

We can’t stress enough on the value of existing literature. The results should point you towards an outstanding issue, give suggestion for future gaps, and make it possible to delineate gaps in existing knowledge.

Research Paper Writing Help

Finding a research problem is just one part of the research paper assignment. You have to develop a research question, formulate a hypothesis, write a thesis statement,  and then write your research paper. It can be a lot of work, which demands a lot of attention and time.

If you need help to brainstorm, research, and write your research paper, click the button below to place your order. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

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A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE:   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

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Research Problem – Explanation & Examples

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A research problem sets the course of investigation in any research process . It can probe practical issues with the aim of suggesting modifications, or scrutinize theoretical quandaries to augment the current understanding in a discipline.

In this article, we delve into the crucial role of a research problem in the research process, as well as offer guidance on how to properly articulate it to steer your research endeavors.

Inhaltsverzeichnis

• 1 Research Problem – In a Nutshell
• 2 Definition: Research problem
• 3 Why is the research problem important?
• 4 Step 1: Finding a general research problem area
• 5 Step 2: Narrowing down the research problem
• 6 Example of a research problem

Research Problem – In a Nutshell

• A research problem is an issue that raises concern about a particular topic.
• Researchers formulate research problems by examining other literature on the topic and assessing the significance and relevance of the problem.
• Creating a research problem involves an overview of a broad problem area and then narrowing it down to the specifics by creating a framework for the topic.
• General problem areas used in formulating research problems include workplace and theoretical research.

Definition: Research problem

A research problem is a specific challenge or knowledge gap that sets the foundation for research. It is the primary statement about a topic in a field of study, and the findings from a research undertaking provide solutions to the research problem.

The research problem is the defining statement that informs the sources and methodologies to be applied to find and recommend proposals for the area of contention.

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Why is the research problem important?

Research should adopt a precise approach for analysis to be relevant and applicable in a real-world context. Researchers can pick any area of study, and in most cases, the topic in question will have a broad scope; a well-formulated problem forms the basis of a strong research paper which illustrates a clear focus.

Writing a research problem is the first step in planning for a research paper, and a well-structured problem prevents a runaway project that lacks a clear direction.

Step 1: Finding a general research problem area

Your primary goal should be to find gaps and meaningful ways your research project offers a solution to a problem or broadens the knowledge bank in the field.

A good approach is to read and hold discussions about the topic , identify areas with insufficient information, highlight areas of contention and form more in-depth conclusions in under-researched areas.

Workplace research

You can carry out workplace research using a practical approach . This aims to identify a problem by analyzing reports, engaging with people in the organization or field of interest, and examining previous research. Some pointers include:

• Efficiency and performance-related issues within an organization.
• Areas or processes that can be improved in the organization.
• Matters of concern among professionals in the field of study.
• Challenges faced by identifiable groups in society.
• Crime in a particular region has been decreasing compared to the rest of the country.
• Stores in one location of a chain have been reporting lower sales in contrast with others in other parts of the country.
• One subsidiary of a company is experiencing high staff turnover, affecting the group’s bottom line.

In theoretical research , researchers aim to offer new insights which contribute to the larger knowledge body in the field rather than proposing change. You can formulate a problem by studying recent studies, debates, and theories to identify gaps. Identifying a research problem in theoretical research may examine the following:

• A context or phenomenon that has not been extensively studied.
• A contrast between two or more thought patterns.
• A position that is not clearly understood.
• A bothersome scenario or question that remains unsolved.

Theoretical problems don’t focus on solving a practical problem but have practical implications in their field. Many theoretical frameworks offer a guide to other practical and applied research scenarios.

• The relationship between genetics and mental issues in adulthood is not clearly understood.
• The effects of racial differences in long-term relationships are yet to be investigated in the modern dating scene.
• Social scientists disagree on the impact of neocolonialism on the socio-economic conditions of black people.

Step 2: Narrowing down the research problem

After identifying a general problem area, you need to zero in on the specific aspect you want to analyze further in the context of your research.

The problem can be narrowed down using the following criteria to create a relevant problem whose solutions adequately answer the research questions . Some questions you can ask to understand the contextual framework of the research problem include:

Significance

Evaluating the significance of a research problem is a necessary step for identifying issues that contribute to the solution of an issue. There are several ways of determining the significance of a research problem. The following questions can help you to evaluate the significance and relevance of a proposed research problem:

• Which area, group or time do you plan to situate your study?
• What attributes will you examine?
• What is the repercussion of not solving the problem?
• Who stands to benefit if the problem is resolved?

Example of a research problem

A fashion retail chain is attempting to increase the number of visitors to its stores, but the management is unaware of the measures to achieve this.

To improve its sales and compete with other chains, the chain requires research into ways of increasing traffic in its stores.

By narrowing down the research problem, you can create the problem statement , hypothesis , and relevant research questions .

What is an example of a research problem?

There has been an upward trend in the immigration of professionals from other countries to the UK. Research is needed to determine the likely causes and effects.

How do you formulate a research problem?

Begin by examining available sources and previous research on your topic of interest. You can narrow down the scope from the literature or observable phenomenon and focus on under-researched areas.

How can you determine the significance of a research problem?

Investigate the specific aspects you would like to investigate. Furthermore, you can determine the consequences of the problem remaining unresolved and the biggest beneficiaries if a solution is found.

What is the context in a research problem?

Context refers to the nature of the problem. It entails studying existing work on the issue, who is affected by it, and the proposed solutions.

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What is Research Problem? Components, Identifying, Formulating,

• Post last modified: 13 August 2023
• Reading time: 10 mins read
• Post category: Research Methodology

What is Research Problem?

A research problem refers to an area or issue that requires investigation, analysis, and resolution through a systematic and scientific approach. It is a specific question, gap, or challenge within a particular field of study that researchers aim to address through their research endeavors.

Table of Content

• 1 What is Research Problem?
• 2 Concept of a Research Problem
• 3 Need to Define a Research Problem
• 4 Conditions and Components of a Research Problem
• 5 Identifying a Research Problem
• 6 Formulating a Research Problem

Concept of a Research Problem

The first step in any research project is to identify the problem. When we specifically talk about research related to a business organisation, the first step is to identify the problem that is being faced by the concerned organisation. The researchers need to develop a concrete, unambiguous and easily comprehensible definition of the problem that requires research.

If the research problem is not well-defined, the research project may be affected. You may also consider defining research problem and carrying out literature review as the foundation on which the entire research process is based.

In general, a research problem refers to a problem that a researcher has witnessed or experienced in a theoretical or real-life situation and wants to develop a solution for the same. The research problem is only a problem statement and it does not describe how to do something. It must be remembered that a research problem is always related to some kind of management dilemma

Need to Define a Research Problem

The researchers must clearly define or formulate the research problem in order to represent a clear picture of what they wish to achieve through their research. When a researcher starts off his research with a well-formulated research problem, it becomes easier to carry out the research.

Some of the major reasons for which a research problem must be defined are:

• Select useful information for research
• Segregate useful information from irrelevant information
• Monitor the research progress
• Ensure research is centred around a problem
• What data should be collected?
• What data attributes are relevant and need to be analysed?
• What relationships should be investigated?
• Determine the structure of the study
• Ensure that the research is centred around the research problem only

Defining a research problem well helps the decision makers in getting good research results if right questions are asked. On the contrary, correct answer to a wrong question will lead to bad research results.

Conditions and Components of a Research Problem

Conditions necessary for the existence of a research problem are:

• Existence of a problem whose solution is not known currently
• Existence of an individual, group or organisation to which the given problem can be attributed
• Existence of at least two alternative courses of action that can be pursued by a researcher
• At least two feasible outcomes of the course of action and out of two outcomes, one outcome should be more preferable to the other

A research problem consists of certain specific components as follows:

• Manager/Decision-maker (individual/group/institution) and his/ her objectives The individual, group or an institution is the one who is facing the problem. At times, the different individuals or groups related to a problem do not agree with the problem statement as their objectives differ from one another. The decision makers must agree on a concrete and clearly worded problem statemen.
• Environment or context of the problem
• Nature of the problem
• Alternative courses of problem
• A set of consequences related to courses of action and the occurrence of events that are not under the control of the manager/decision maker
• A state of uncertainty for which a course of action is best

Identifying a Research Problem

Identifying a research problem is an important and time-consuming activity. Research problem identification involves understanding the given social problem that needs to be investigated in order to solve it. In most cases, the researchers usually identify a research problem by using their observation, knowledge, wisdom and skills. Identifying a research problem can be as simple as recognising the difficulties and problems in your workplace.

Certain other factors that are considered while identifying a research problem include:

• Potential research problems raised at the end of journal articles
• Large-scale reports and data records in the field may disclose the findings or facts based on data that require further investigation
• Personal interest of the researcher
• Knowledge and competence of the researcher
• Availability of resources such as large-scale data collection, time and finance
• Relative importance of different problems
• Practical utility of finding answers to a problem
• Data availability for a problem

Formulating a Research Problem

Formulating a research problem is usually done under the first step of research process, i.e., defining the research problem. Identification, clarification and formulation of a research problem is done using different steps as:

• Discover the Management Dilemma
• Define the Management Question
• Define the Research Question
• Refine the Research Question(s)

You have already studied why it is important to clarify a research question. The next step is to discover the management dilemma. The entire research process starts with a management dilemma. For instance, an organisation facing increasing number of customer complaints may want to carry out research.

At most times, the researchers state the management dilemma followed by developing questions which are then broken down into specific set of questions. Management dilemma, in most cases, is a symptom of the actual problem being faced by an organisation.

A few examples of management dilemma are low turnover, high attrition, high product defect rate, low quality, increasing costs, decreasing profits, low employee morale, high absenteeism, flexibility and remote work issues, use of technology, increasing market share of a competitor, decline in plant/production capacity, distribution of profit between dividends and retained earnings, etc.

If an organisation tracks its performance indicators on a regular basis, it is quite easy to identify the management dilemma. Now, the difficult task for a researcher to choose a particular management dilemma among the given set of management dilemmas.

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• Expansion Strategies
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• Retrenchment Strategies
• Competitive Advantage
• Strategic Choice and Strategic Alternatives
• What is Production Process?
• What is Process Technology?
• What is Process Improvement?
• Strategic Capacity Management
• Production and Logistics Strategy
• Taxonomy of Supply Chain Strategies
• Factors Considered in Supply Chain Planning
• Operational and Strategic Issues in Global Logistics
• Logistics Outsourcing Strategy
• What is Supply Chain Mapping?
• Supply Chain Process Restructuring
• Points of Differentiation
• Re-engineering Improvement in SCM
• What is Supply Chain Drivers?
• Supply Chain Operations Reference (SCOR) Model
• Customer Service and Cost Trade Off
• Internal and External Performance Measures
• Linking Supply Chain and Business Performance
• Netflix’s Niche Focused Strategy
• Disney and Pixar Merger
• Process Planning at Mcdonald’s

Service Operations Management

• What is Service?
• What is Service Operations Management?
• What is Service Design?
• Service Design Process
• Service Delivery
• What is Service Quality?
• Gap Model of Service Quality
• Juran Trilogy
• Service Performance Measurement
• Service Decoupling
• IT Service Operation
• Service Operations Management in Different Sector

Procurement Management

• What is Procurement Management?
• Procurement Negotiation
• Types of Requisition
• RFX in Procurement
• What is Purchasing Cycle?
• Vendor Managed Inventory
• Internal Conflict During Purchasing Operation
• Spend Analysis in Procurement
• Sourcing in Procurement
• Supplier Evaluation and Selection in Procurement
• Blacklisting of Suppliers in Procurement
• Total Cost of Ownership in Procurement
• Incoterms in Procurement
• Documents Used in International Procurement
• Transportation and Logistics Strategy
• What is Capital Equipment?
• Procurement Process of Capital Equipment
• Acquisition of Technology in Procurement
• What is E-Procurement?
• E-marketplace and Online Catalogues
• Fixed Price and Cost Reimbursement Contracts
• Contract Cancellation in Procurement
• Ethics in Procurement
• Legal Aspects of Procurement
• Global Sourcing in Procurement
• Intermediaries and Countertrade in Procurement

Strategic Management

• What is Strategic Management?
• What is Value Chain Analysis?
• Mission Statement
• Business Level Strategy
• What is SWOT Analysis?
• What is Competitive Advantage?
• What is Vision?
• What is Ansoff Matrix?
• Prahalad and Gary Hammel
• Strategic Management In Global Environment
• Competitor Analysis Framework
• Competitive Rivalry Analysis
• Competitive Dynamics
• What is Competitive Rivalry?
• Five Competitive Forces That Shape Strategy
• What is PESTLE Analysis?
• Fragmentation and Consolidation Of Industries
• What is Technology Life Cycle?
• What is Diversification Strategy?
• What is Corporate Restructuring Strategy?
• Resources and Capabilities of Organization
• Role of Leaders In Functional-Level Strategic Management
• Functional Structure In Functional Level Strategy Formulation
• Information And Control System
• What is Strategy Gap Analysis?
• Issues In Strategy Implementation
• Matrix Organizational Structure
• What is Strategic Management Process?

Supply Chain

• What is Supply Chain Management?
• Supply Chain Planning and Measuring Strategy Performance
• What is Warehousing?
• What is Packaging?
• What is Inventory Management?
• What is Material Handling?
• What is Order Picking?
• Receiving and Dispatch, Processes
• What is Warehouse Design?
• What is Warehousing Costs?

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45 Research Problem Examples & Inspiration

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A research problem is an issue of concern that is the catalyst for your research. It demonstrates why the research problem needs to take place in the first place.

Generally, you will write your research problem as a clear, concise, and focused statement that identifies an issue or gap in current knowledge that requires investigation.

The problem will likely also guide the direction and purpose of a study. Depending on the problem, you will identify a suitable methodology that will help address the problem and bring solutions to light.

Research Problem Examples

In the following examples, I’ll present some problems worth addressing, and some suggested theoretical frameworks and research methodologies that might fit with the study. Note, however, that these aren’t the only ways to approach the problems. Keep an open mind and consult with your dissertation supervisor!

Psychology Problems

1. Social Media and Self-Esteem: “How does prolonged exposure to social media platforms influence the self-esteem of adolescents?”

• Theoretical Framework : Social Comparison Theory
• Methodology : Longitudinal study tracking adolescents’ social media usage and self-esteem measures over time, combined with qualitative interviews.

2. Sleep and Cognitive Performance: “How does sleep quality and duration impact cognitive performance in adults?”

• Theoretical Framework : Cognitive Psychology
• Methodology : Experimental design with controlled sleep conditions, followed by cognitive tests. Participant sleep patterns can also be monitored using actigraphy.

3. Childhood Trauma and Adult Relationships: “How does unresolved childhood trauma influence attachment styles and relationship dynamics in adulthood?

• Theoretical Framework : Attachment Theory
• Methodology : Mixed methods, combining quantitative measures of attachment styles with qualitative in-depth interviews exploring past trauma and current relationship dynamics.

4. Mindfulness and Stress Reduction: “How effective is mindfulness meditation in reducing perceived stress and physiological markers of stress in working professionals?”

• Theoretical Framework : Humanist Psychology
• Methodology : Randomized controlled trial comparing a group practicing mindfulness meditation to a control group, measuring both self-reported stress and physiological markers (e.g., cortisol levels).

5. Implicit Bias and Decision Making: “To what extent do implicit biases influence decision-making processes in hiring practices?

• Theoretical Framework : Cognitive Dissonance Theory
• Methodology : Experimental design using Implicit Association Tests (IAT) to measure implicit biases, followed by simulated hiring tasks to observe decision-making behaviors.

6. Emotional Regulation and Academic Performance: “How does the ability to regulate emotions impact academic performance in college students?”

• Theoretical Framework : Cognitive Theory of Emotion
• Methodology : Quantitative surveys measuring emotional regulation strategies, combined with academic performance metrics (e.g., GPA).

7. Nature Exposure and Mental Well-being: “Does regular exposure to natural environments improve mental well-being and reduce symptoms of anxiety and depression?”

• Theoretical Framework : Biophilia Hypothesis
• Methodology : Longitudinal study comparing mental health measures of individuals with regular nature exposure to those without, possibly using ecological momentary assessment for real-time data collection.

8. Video Games and Cognitive Skills: “How do action video games influence cognitive skills such as attention, spatial reasoning, and problem-solving?”

• Theoretical Framework : Cognitive Load Theory
• Methodology : Experimental design with pre- and post-tests, comparing cognitive skills of participants before and after a period of action video game play.

9. Parenting Styles and Child Resilience: “How do different parenting styles influence the development of resilience in children facing adversities?”

• Theoretical Framework : Baumrind’s Parenting Styles Inventory
• Methodology : Mixed methods, combining quantitative measures of resilience and parenting styles with qualitative interviews exploring children’s experiences and perceptions.

10. Memory and Aging: “How does the aging process impact episodic memory , and what strategies can mitigate age-related memory decline?

• Theoretical Framework : Information Processing Theory
• Methodology : Cross-sectional study comparing episodic memory performance across different age groups, combined with interventions like memory training or mnemonic strategies to assess potential improvements.

Education Problems

11. Equity and Access : “How do socioeconomic factors influence students’ access to quality education, and what interventions can bridge the gap?

• Theoretical Framework : Critical Pedagogy
• Methodology : Mixed methods, combining quantitative data on student outcomes with qualitative interviews and focus groups with students, parents, and educators.

12. Digital Divide : How does the lack of access to technology and the internet affect remote learning outcomes, and how can this divide be addressed?

• Theoretical Framework : Social Construction of Technology Theory
• Methodology : Survey research to gather data on access to technology, followed by case studies in selected areas.

13. Teacher Efficacy : “What factors contribute to teacher self-efficacy, and how does it impact student achievement?”

• Theoretical Framework : Bandura’s Self-Efficacy Theory
• Methodology : Quantitative surveys to measure teacher self-efficacy, combined with qualitative interviews to explore factors affecting it.

14. Curriculum Relevance : “How can curricula be made more relevant to diverse student populations, incorporating cultural and local contexts?”

• Theoretical Framework : Sociocultural Theory
• Methodology : Content analysis of curricula, combined with focus groups with students and teachers.

15. Special Education : “What are the most effective instructional strategies for students with specific learning disabilities?

• Theoretical Framework : Social Learning Theory
• Methodology : Experimental design comparing different instructional strategies, with pre- and post-tests to measure student achievement.

16. Dropout Rates : “What factors contribute to high school dropout rates, and what interventions can help retain students?”

• Methodology : Longitudinal study tracking students over time, combined with interviews with dropouts.

17. Bilingual Education : “How does bilingual education impact cognitive development and academic achievement?

• Methodology : Comparative study of students in bilingual vs. monolingual programs, using standardized tests and qualitative interviews.

18. Classroom Management: “What reward strategies are most effective in managing diverse classrooms and promoting a positive learning environment?

• Theoretical Framework : Behaviorism (e.g., Skinner’s Operant Conditioning)
• Methodology : Observational research in classrooms , combined with teacher interviews.

19. Standardized Testing : “How do standardized tests affect student motivation, learning, and curriculum design?”

• Theoretical Framework : Critical Theory
• Methodology : Quantitative analysis of test scores and student outcomes, combined with qualitative interviews with educators and students.

20. STEM Education : “What methods can be employed to increase interest and proficiency in STEM (Science, Technology, Engineering, and Mathematics) fields among underrepresented student groups?”

• Theoretical Framework : Constructivist Learning Theory
• Methodology : Experimental design comparing different instructional methods, with pre- and post-tests.

21. Social-Emotional Learning : “How can social-emotional learning be effectively integrated into the curriculum, and what are its impacts on student well-being and academic outcomes?”

• Theoretical Framework : Goleman’s Emotional Intelligence Theory
• Methodology : Mixed methods, combining quantitative measures of student well-being with qualitative interviews.

22. Parental Involvement : “How does parental involvement influence student achievement, and what strategies can schools use to increase it?”

• Theoretical Framework : Reggio Emilia’s Model (Community Engagement Focus)
• Methodology : Survey research with parents and teachers, combined with case studies in selected schools.

23. Early Childhood Education : “What are the long-term impacts of quality early childhood education on academic and life outcomes?”

• Theoretical Framework : Erikson’s Stages of Psychosocial Development
• Methodology : Longitudinal study comparing students with and without early childhood education, combined with observational research.

24. Teacher Training and Professional Development : “How can teacher training programs be improved to address the evolving needs of the 21st-century classroom?”

• Theoretical Framework : Adult Learning Theory (Andragogy)
• Methodology : Pre- and post-assessments of teacher competencies, combined with focus groups.

25. Educational Technology : “How can technology be effectively integrated into the classroom to enhance learning, and what are the potential drawbacks or challenges?”

• Theoretical Framework : Technological Pedagogical Content Knowledge (TPACK)
• Methodology : Experimental design comparing classrooms with and without specific technologies, combined with teacher and student interviews.

Sociology Problems

26. Urbanization and Social Ties: “How does rapid urbanization impact the strength and nature of social ties in communities?”

• Theoretical Framework : Structural Functionalism
• Methodology : Mixed methods, combining quantitative surveys on social ties with qualitative interviews in urbanizing areas.

27. Gender Roles in Modern Families: “How have traditional gender roles evolved in families with dual-income households?”

• Theoretical Framework : Gender Schema Theory
• Methodology : Qualitative interviews with dual-income families, combined with historical data analysis.

28. Social Media and Collective Behavior: “How does social media influence collective behaviors and the formation of social movements?”

• Theoretical Framework : Emergent Norm Theory
• Methodology : Content analysis of social media platforms, combined with quantitative surveys on participation in social movements.

29. Education and Social Mobility: “To what extent does access to quality education influence social mobility in socioeconomically diverse settings?”

• Methodology : Longitudinal study tracking educational access and subsequent socioeconomic status, combined with qualitative interviews.

30. Religion and Social Cohesion: “How do religious beliefs and practices contribute to social cohesion in multicultural societies?”

• Methodology : Quantitative surveys on religious beliefs and perceptions of social cohesion, combined with ethnographic studies.

31. Consumer Culture and Identity Formation: “How does consumer culture influence individual identity formation and personal values?”

• Theoretical Framework : Social Identity Theory
• Methodology : Mixed methods, combining content analysis of advertising with qualitative interviews on identity and values.

32. Migration and Cultural Assimilation: “How do migrants negotiate cultural assimilation and preservation of their original cultural identities in their host countries?”

• Theoretical Framework : Post-Structuralism
• Methodology : Qualitative interviews with migrants, combined with observational studies in multicultural communities.

33. Social Networks and Mental Health: “How do social networks, both online and offline, impact mental health and well-being?”

• Theoretical Framework : Social Network Theory
• Methodology : Quantitative surveys assessing social network characteristics and mental health metrics, combined with qualitative interviews.

34. Crime, Deviance, and Social Control: “How do societal norms and values shape definitions of crime and deviance, and how are these definitions enforced?”

• Theoretical Framework : Labeling Theory
• Methodology : Content analysis of legal documents and media, combined with ethnographic studies in diverse communities.

35. Technology and Social Interaction: “How has the proliferation of digital technology influenced face-to-face social interactions and community building?”

• Theoretical Framework : Technological Determinism
• Methodology : Mixed methods, combining quantitative surveys on technology use with qualitative observations of social interactions in various settings.

Nursing Problems

36. Patient Communication and Recovery: “How does effective nurse-patient communication influence patient recovery rates and overall satisfaction with care?”

• Methodology : Quantitative surveys assessing patient satisfaction and recovery metrics, combined with observational studies on nurse-patient interactions.

37. Stress Management in Nursing: “What are the primary sources of occupational stress for nurses, and how can they be effectively managed to prevent burnout?”

• Methodology : Mixed methods, combining quantitative measures of stress and burnout with qualitative interviews exploring personal experiences and coping mechanisms.

38. Hand Hygiene Compliance: “How effective are different interventions in improving hand hygiene compliance among nursing staff, and what are the barriers to consistent hand hygiene?”

• Methodology : Experimental design comparing hand hygiene rates before and after specific interventions, combined with focus groups to understand barriers.

39. Nurse-Patient Ratios and Patient Outcomes: “How do nurse-patient ratios impact patient outcomes, including recovery rates, complications, and hospital readmissions?”

• Methodology : Quantitative study analyzing patient outcomes in relation to staffing levels, possibly using retrospective chart reviews.

40. Continuing Education and Clinical Competence: “How does regular continuing education influence clinical competence and confidence among nurses?”

• Methodology : Longitudinal study tracking nurses’ clinical skills and confidence over time as they engage in continuing education, combined with patient outcome measures to assess potential impacts on care quality.

Communication Studies Problems

41. Media Representation and Public Perception: “How does media representation of minority groups influence public perceptions and biases?”

• Theoretical Framework : Cultivation Theory
• Methodology : Content analysis of media representations combined with quantitative surveys assessing public perceptions and attitudes.

42. Digital Communication and Relationship Building: “How has the rise of digital communication platforms impacted the way individuals build and maintain personal relationships?”

• Theoretical Framework : Social Penetration Theory
• Methodology : Mixed methods, combining quantitative surveys on digital communication habits with qualitative interviews exploring personal relationship dynamics.

43. Crisis Communication Effectiveness: “What strategies are most effective in managing public relations during organizational crises, and how do they influence public trust?”

• Theoretical Framework : Situational Crisis Communication Theory (SCCT)
• Methodology : Case study analysis of past organizational crises, assessing communication strategies used and subsequent public trust metrics.

44. Nonverbal Cues in Virtual Communication: “How do nonverbal cues, such as facial expressions and gestures, influence message interpretation in virtual communication platforms?”

• Theoretical Framework : Social Semiotics
• Methodology : Experimental design using video conferencing tools, analyzing participants’ interpretations of messages with varying nonverbal cues.

45. Influence of Social Media on Political Engagement: “How does exposure to political content on social media platforms influence individuals’ political engagement and activism?”

• Theoretical Framework : Uses and Gratifications Theory
• Methodology : Quantitative surveys assessing social media habits and political engagement levels, combined with content analysis of political posts on popular platforms.

Before you Go: Tips and Tricks for Writing a Research Problem

This is an incredibly stressful time for research students. The research problem is going to lock you into a specific line of inquiry for the rest of your studies.

So, here’s what I tend to suggest to my students:

• Start with something you find intellectually stimulating – Too many students choose projects because they think it hasn’t been studies or they’ve found a research gap. Don’t over-estimate the importance of finding a research gap. There are gaps in every line of inquiry. For now, just find a topic you think you can really sink your teeth into and will enjoy learning about.
• Take 5 ideas to your supervisor – Approach your research supervisor, professor, lecturer, TA, our course leader with 5 research problem ideas and run each by them. The supervisor will have valuable insights that you didn’t consider that will help you narrow-down and refine your problem even more.
• Trust your supervisor – The supervisor-student relationship is often very strained and stressful. While of course this is your project, your supervisor knows the internal politics and conventions of academic research. The depth of knowledge about how to navigate academia and get you out the other end with your degree is invaluable. Don’t underestimate their advice.

I’ve got a full article on all my tips and tricks for doing research projects right here – I recommend reading it:

• 9 Tips on How to Choose a Dissertation Topic

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

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A Guide to the Types of Research and How They're Used

Know before you read At SNHU, we want to make sure you have the information you need to make decisions about your education and your future—no matter where you choose to go to school. That's why our informational articles may reference careers for which we do not offer academic programs, along with salary data for those careers. Cited projections do not guarantee actual salary or job growth.

Research is the discovery of new ideas. By identifying a problem and devising solutions using source-based evidence, you can apply research to just about any academic area and to many professions, according to Dr. Matthew Schandler , an adjunct instructor of history and academic partner at Southern New Hampshire University (SNHU). Schandler applies his background in political science, data science and history of technology to his work teaching a history research capstone course  at SNHU.

Jeremy Pedigo , an adjunct instructor of history and academic partner at SNHU, said that research allows you to discover new ideas that are relevant to your academic field and profession. The process involves answering research questions using scholarly sources . Pedigo is a doctoral student in history himself (doctorates in history are not currently available at SNHU).

Overview of Research

Research is about much more than searching for keywords in a search engine. Schandler said that any type of academic research should center around first identifying a problem and then devising solutions to that problem. The process for devising those solutions depends largely on your content area.

According to the National Institutes of Health ( NIH ), quantitative research is the more empirical of the two types, meaning it's based on observation or experience rather than logic. Generally applied to the research conducted with clinical studies or that has measurable outcomes, data tends to be numerical or deductive. Conclusions are typically based on results from studies and various survey methods. This type of research is well-suited for testing hypotheses and establishing cause-and-effect relationships.

Qualitative research tends to be more narrative in scope, as explained by NIH . Interviews measuring viewpoints and opinions, as well as historical or literary studies, are commonly used for qualitative studies. With this type of research, examining theories and describing decision-making or communication processes is common.

What are the Basic Types of Research?

Once you have determined your research question, you’ll need to decide if you are going to apply a fundamental or applied approach, according to Pedigo.

▸ Fundamental and Applied Research

Fundamental research , as its name implies, is the most basic type of research. “It seeks to answer a general question or find a causal relationship between multiple factors,” Pedigo said. This is particularly useful in undergraduate courses when students are building a foundation of knowledge in their subject area.

Pedigo lists these as common sources frequently used to conduct fundamental research:

• Books, letters and private papers
• Credible websites and interviews
• Newspaper and magazine articles
• Manuscripts
• Peer-reviewed articles

Applied research seeks to understand societal problems and find solutions to improve everyday life. This involves applying concepts in business, natural sciences and behavioral and social sciences to improve aspects of society. Applied research allows you to apply what you’ve learned to solving problems, Pedigo said.

Common sources of applied research, according to Pedigo, are:

• Academic books
• Focus groups and surveys
• Government reports

▸ Theoretical and Experimental Research

“Theoretical research attempts to measure a theory or phenomenon to determine its relevancy based on research findings,” Pedigo said. “Whereas experimental research is the study of two or more variables with a control group and an experimental group.”

Deciding which to use depends a great deal on the type of problem you're trying to solve:

• Theoretical research is rooted in hypothetical situations.
• Experimental research is where theories are tested for validity.

Physics is another area where theoretical and experimental research types are commonly applied, according to Schandler.

“Theoretical physicists develop models to consider inexplicable phenomena,” he said. For example, famed astronomer Edwin Hubble conducted theoretical research to try to prove that nebulae existed beyond the Milky Way. Hubble later developed new telescopic equipment to test his theory using experimental research.

Which Careers Focus on Research?

Research skills can enhance virtually any career. Some careers, like scientists or college professors, focus on research as a core aspect of the work. Many other careers benefit from people who bring strong research skills to their roles.

According to Pedigo, some examples of careers where strong research skills could be particularly helpful are:

• Data Scientist , where you could use research skills to gather and analyze data to develop algorithms or recommend systems and processes. According to the U.S. Bureau of Labor Statistics (BLS), the median salary for jobs in this role was $103,500 in 2022.* Explore a master's degree in data analytics .
• Mechanical Engineer , where you could research new ways to build or enhance mechanical systems, or design new energy systems or other processes to solve problems. The median salary for jobs in this role was $96,310 in 2022, according to BLS.* Explore a degree in mechanical engineering .
• Environmental Scientist , where research into climate change, pollution or water sources could help you make the world a cleaner and healthier place. BLS shows the median salary for this position was $76,480 in 2022.* Explore a degree in environmental science .
• Historian , where you could specialize in any number of areas. Historians may work at universities, nonprofits, governmental organizations or museums, to name just a few of the possibilities. While salaries can vary widely, BLS lists the median salary for historians as $64,540 in 2022.* Explore a degree in history .
• Management Analyst , where you could work in a variety of businesses researching market trends and making recommendations to improve business operations. The median salary for jobs in this role was $95,290 per year in 2022, according to BLS.* Explore a Master of Business Administration .

Find Your Program

What are the top skills needed for a research career.

Having the desire to learn a new topic and discover new findings is critical to becoming a strong researcher, according to Pedigo. He noted the following skills in particular as stand-outs for conducting research, regardless of field:

• Computational proficiency , including using online databases. Strong computer search skills enhance the ability to seek out information from different perspectives.
• Documentation and note-taking , both of which are an absolute must, according to Schandler. “If you don’t read materials actively and accurately, you won’t make the nuanced connections needed to draw accurate conclusions,” he said.
• Literary ability , which includes writing outlines, taking notes of research papers and writing in the appropriate academic style for your discipline is critically important, according to both Pedigo and Schandler.
• Intellectual curiosity is a must-have trait. “If one lacks a drive for knowledge, they might not ask the important questions essential to guiding the research process,” Schandler said.
• Organizational skills are also vital. Working with research can involve working with large volumes of information. Developing a process to keep research structured is necessary.

Pedigo also noted that taking full advantage of academic resources through your university library is helpful. “You’ll want to become familiar with your library and its staff to learn about the types of sources and services available to students, faculty and staff, and how they can help aid you in your research,” he said.

Those resources can be very helpful when it comes to ensuring that you write with integrity and report your research findings accurately.

The Key to Conducting Research

Regardless of the method you use, the most important aspect of conducting any kind of research is that it leads to actionable outcomes. By staying focused on the core question you're trying to answer, researchers in any discipline can help increase knowledge in their field and find new ways for information to cross over into other disciplines.

Schandler said that “in a world rife with disinformation and misinformation, research steers analysts towards deeper understandings.” He feels that the interdisciplinary, collaborative sharing of research findings ensures creative solutions to the world’s great problems, past and present.

A degree can change your life. Choose your program  from 200+ SNHU degrees that can take you where you want to go.

*Cited job growth projections may not reflect local and/or short-term economic or job conditions and do not guarantee actual job growth. Actual salaries and/or earning potential may be the result of a combination of factors including, but not limited to: years of experience, industry of employment, geographic location, and worker skill.

Marie Morganelli, PhD, is an educator, writer and editor.

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• How to Define a Research Problem | Ideas & Examples

How to Define a Research Problem | Ideas & Examples

Published on 8 November 2022 by Shona McCombes and Tegan George.

A research problem is a specific issue or gap in existing knowledge that you aim to address in your research. You may choose to look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you think will fit best.

This article helps you identify and refine a research problem. When writing your research proposal or introduction , formulate it as a problem statement and/or research questions .

Table of contents

Why is the research problem important, step 1: identify a broad problem area, step 2: learn more about the problem, frequently asked questions about research problems.

Having an interesting topic isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a clear problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis , starting a research paper , or writing a research proposal , the research problem is the first step towards knowing exactly what you’ll do and why.

Prevent plagiarism, run a free check.

As you read about your topic, look for under-explored aspects or areas of concern, conflict, or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, or talking to people who work in the relevant field or organisation. You might look for:

• Issues with performance or efficiency
• Processes that could be improved
• Areas of concern among practitioners
• Difficulties faced by specific groups of people

Examples of practical research problems

Voter turnout in New England has been decreasing, in contrast to the rest of the country.

The HR department of a local chain of restaurants has a high staff turnover rate.

A non-profit organisation faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

If you are doing theoretical research, you can identify a research problem by reading existing research, theory, and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied
• A contradiction between two or more perspectives
• A situation or relationship that is not well understood
• A troubling question that has yet to be resolved

Examples of theoretical research problems

The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

The relationship between gender, race, and income inequality has yet to be closely studied in the context of the millennial gig economy.

Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?
• Is it a newly-discovered problem, or a well-established one?
• What research has already been done?
• What, if any, solutions have been proposed?
• What are the current debates about the problem? What is missing from these debates?

Specificity and relevance

• What particular place, time, and/or group of people will you focus on?
• What aspects will you not be able to tackle?
• What will the consequences be if the problem is not resolved?

Example of a specific research problem

A local non-profit organisation focused on alleviating food insecurity has always fundraised from its existing support base. It lacks understanding of how best to target potential new donors. To be able to continue its work, the organisation requires research into more effective fundraising strategies.

Once you have narrowed down your research problem, the next step is to formulate a problem statement , as well as your research questions or hypotheses .

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement.

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis – a prediction that will be confirmed or disproved by your research.

Research objectives describe what you intend your research project to accomplish.

They summarise the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

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• Published: 09 September 2024

Bridging auditory perception and natural language processing with semantically informed deep neural networks

• Michele Esposito 1 ,
• Giancarlo Valente 1 ,
• Yenisel Plasencia-Calaña 3 ,
• Michel Dumontier 4 ,
• Bruno L. Giordano 2 &
• Elia Formisano 1 , 3  

Scientific Reports volume  14 , Article number:  20994 ( 2024 ) Cite this article

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• Auditory system
• Cognitive neuroscience
• Computational neuroscience
• Neuroscience

Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently surpassed traditional machine learning in sound classification. However, current DNNs map sounds to labels using binary categorical variables, neglecting the semantic relations between labels. Cognitive neuroscience research suggests that human listeners exploit such semantic information besides acoustic cues. Hence, our hypothesis is that incorporating semantic information improves DNN’s sound recognition performance, emulating human behaviour. In our approach, sound recognition is framed as a regression problem, with CNNs trained to map spectrograms to continuous semantic representations from NLP models (Word2Vec, BERT, and CLAP text encoder). Two DNN types were trained: semDNN with continuous embeddings and catDNN with categorical labels, both with a dataset extracted from a collection of 388,211 sounds enriched with semantic descriptions. Evaluations across four external datasets, confirmed the superiority of semantic labeling from semDNN compared to catDNN, preserving higher-level relations. Importantly, an analysis of human similarity ratings for natural sounds, showed that semDNN approximated human listener behaviour better than catDNN, other DNNs, and NLP models. Our work contributes to understanding the role of semantics in sound recognition, bridging the gap between artificial systems and human auditory perception.

Introduction

Recognizing sounds involves the conversion of acoustic waveforms into meaningful descriptions of the sound-producing sources and events. Automatic and effortless in humans, sound recognition poses a considerable challenge for artificial hearing. Various machine learning (ML) algorithms have been proposed that treat sound recognition as a classification problem. Typically, these algorithms entail the initial extraction of diverse features from the acoustic waveform, which are further analyzed and assigned to predefined classes 1 . In recent developments, deep neural networks (DNNs) have emerged as superior to traditional ML algorithms in sound recognition tasks. Following parallel advancements observed in visual object recognition research 2 , convolutional neural networks (CNNs) have been used for sound classification tasks 3 , 4 , 5 (here, referred to as sound-to-event CNNs). Trained on a large-scale dataset of human-labeled sounds (Audioset 6 ), Google’s VGGish and Yamnet yield remarkable performance. These networks receive spectrogram representations as input and can classify sounds into a large number of classes (527 and 521 classes, for VGGish and Yamnet, respectively). Since their publication, VGGish and Yamnet (and related networks 5 ) have been fine-tuned for applications in several specialized acoustic domains, from neonatal heartbeat and lung sound quality assessment 7 to aircraft detection system 8 and speech-emotion recognition 9 .

In addition to the basic set of labels, Audioset 6 introduced a taxonomy specifying an additional set of super-ordinate labels and their (hierarchical) relations to the basic set. While DNN models frequently employ the Audioset basic labels (or subsets of them) for training purposes 10 , the taxonomic information is generally not utilized (see 11 for an exception). This is because labels are commonly encoded as binary categorical variables using one-hot or multi-hot (in case of simultaneous multiple labels) encoding as depicted in Fig.  1 . DNNs trained with this approach in fact map sounds to a set of orthogonal labels.

Research in cognitive psychology 12 and cognitive neuroscience 13 suggests that human listeners, when engaged in listening to and in comparing real-world sounds, exploit higher-level semantic information about sources in addition to acoustic cues. In a recent study by Giordano et al. 13 , behavioural data involving perceived sound (dis)similarities, assessed through a hierarchical sorting task 14 , were analyzed to investigate the explanatory power of sound-to-event DNNs, such as VGGish and Yamnet, and other models related to acoustic, auditory perception, and lexical-semantic (natural language processing, NLP). The results demonstrated that sound-to-event DNNs surpassed all other models in predicting human judgments of sound dissimilarity, indicating that sound-to-event DNNs provide, at present, the best approximation of human behaviour for sound (dis)similarity judgments. In addition, the results highlighted the ability of NLP models, specifically Word2Vec 15 to capture variance in behavioural data that couldn’t be accounted for by sound-to-event DNNs trained with categorical labels.

Motivated by these findings, the present study sought to develop DNNs that—mimicking human behaviour—incorporate lexical semantic information in the recognition of sounds. To this aim, we formulated sound recognition as a regression problem, training a convolutional DNN to learn the mapping of spectrograms to continuous and distributed semantic representations. In particular, we obtained these representations as the embeddings from NLP models. We considered word-level, pre-trained embeddings: Word2Vec 15 , and context-dependent embeddings: Bidirectional Encoder Representations from Transformers ( BERT ) 16 . Additionally, we considered the semantic embeddings obtained from the Contrastive Language-Audio Pretraining ( CLAP ) text encoder, a contrastive-learning model that brings audio and BERT embeddings into a joint multimodal space 17 .

To evaluate the impact of semantics on sound recognition, we trained two types of DNNs: semDNN, utilizing one of the described continuous semantic embeddings, and catDNN, employing categorical, one-hot encoded labels. To ensure a fair comparison, we trained the DNNs from scratch using a curated dataset of 388,211 sounds from the Super Hard Drive Combo 18 . In this dataset, a rich semantic description of each sound can be derived from the associated metadata. We expected that, compared with a homologous network trained with categorical labels, semDNN would produce semantically more accurate labeling in sound recognition tasks and that semDNN embeddings would preserve higher-level lexical semantic relations between sound sources. Furthermore, we expected that semDNNs would better approximate human behaviour in auditory cognitive tasks compared to catDNNs due to the preservation of semantic relations in NLP embeddings. Our approach differs from previous studies that combined sound-to-event DNNs with language embeddings 17 , 19 , as we specifically focus on evaluating the effects of semantic representation types and predicting human perceptions. In summary, our work aims to bridge the gap between artificial sound recognition systems and human auditory perception by incorporating semantic information into DNNs 20 .

Categorical vs semantic label encoding. Comparison of t-stochastic nearest embedding (t-SNE) 21 visualizations between one-hot encoding ( a ) and Word2Vec ( b ) spaces: Embeddings were made by a one-hot encoding transformation of the words ( a ), or through the use of the GoogleNews-300D Word2Vec model 15 ( b ). In ( a ), words are equidistant from one another, and the proximity of words with semantic relationships follows the order in which the words are listed. However, in ( b ), words that are semantically related are closer to each other, demonstrating a more meaningful representation.

Proposed framework: from preprocessing to evaluation. ( a ) Label encoding strategy : transition from lexical units to either orthogonal (one-hot encoder) representations or continuous representations (word encoder). ( b ) Audio Preprocessing : conversion of waveforms to 1 s patched spectrograms. ( c ) Model Architecture : Note the variation in the final dense layer. ( d ) Evaluation Phase : transition from embeddings to word-level predictions, with the computation of quantitative metrics to gauge model performance. ( e ) Model Comparison : Representational Similarity Analysis (RSA) is adopted to compare the ability of models to predict human behavioural data.

In this section, we outline the methods used in our study (see also Fig.  2 ). We begin with the extraction of raw sound descriptors from a large database. These descriptors are then refined into meaningful sound-describing words with a natural language processing pipeline. Subsequently, these words are transformed into numerical representations, involving both categorical (one-hot encoding), and continuous encoding formats through language models as Word2Vec, BERT, and CLAP. One-hot encoding is applied using a binary vector format where each label is represented as a distinct dimension (see Fig.  2 a).

Raw audio waveforms are then segmented into 1-s patched spectrograms. This step prepares the audio data for neural network analysis by capturing essential sound features, shown in Fig.  2 b. Next, we designed our model architecture as a CNN, incorporating variations in the final dense layer to differentiate between the classification task, for categorical labelling, and the regression task for continuous labelling, (see Fig.  2 c).

In the evaluation phase, we then transition from neural network embeddings to word-level predictions. At this step, we employ various quantitative metrics to assess the model’s performance, and the alignment with expected sound categorizations (see Fig.  2 d). Finally, we use Representational Similarity Analysis (RSA) to evaluate the ability of our models to predict human behavioural data (see Fig.  2 e).

Semantic models

We employed three language models for label transformation: GoogleNews Word2Vec-300D 15 , BERT-768D 16 , and CLAP-1024D text encoder 17 .

Word2Vec is a word-based encoder trained on large corpora to learn distributed representations that capture semantic similarities and relationships between words.

BERT, Bidirectional Encoder Representations from Transformers, is a pre-trained language model that learns to capture deep relationships and context between words in sentences.

Contrastive Language-Audio Pretraining (CLAP) is a transformer-based architecture that is fine-tuned for the audio-to-text task using a large dataset of paired audio and text descriptions encoded with BERT. The text encoder is trained jointly with the audio encoder using a contrastive loss function, which encourages the audio and text representations to be similar in the joint multimodal space. Specifically, the contrastive loss function aims to maximize the similarity between the representations of a given audio-text pair while minimizing the similarity between the representations of different pairs.

Label encoding strategies

To extract semantic embeddings from the sound descriptions we performed the label transformation depicted in Fig.  2 a. For CatDNN, we used one-hot encoding. Each label was represented as a binary vector of 9960 dimensions, as the number of entities contained in the dictionary (see section “ Training dataset ”), with a value of 1 indicating the presence of the label in the description.

To obtain a single embedding describing the sound semantics in SemDNN, we directly used the single-word labels as input to word-based encoders. Specifically, we computed the Word2Vec, BERT or CLAP embeddings for each word present in the label and then averaged these word embeddings. This resulted in a single embedding that captured the overall semantic information of the sound (sound-level embedding). This process was straightforward for Word2Vec, as it produces a single, context-independent embedding for each word. However, for BERT and CLAP, we needed to make some preliminary adjustments before applying the same method.

BERT and CLAP embeddings BERT is a context-dependent language model, which means that the embedding of a single word changes depending on its position in the sentence, the surrounding words, and the sentence length. To obtain word-level BERT embeddings, we first considered all the sentences contained in the SoundIdeas dataset (see section “ Training dataset ”). From these sentences, we generated an initial dictionary consisting of the words present in the sentences. This dictionary was specifically designed for BERT representation and associated each word with two elements: its single-word embedding within a particular sentence and the corresponding sentence itself. This approach was taken in order to capture the variations in single-word BERT embeddings across different sentences where the word appears. The computation of these sentence-dependent word-level BERT embeddings required careful handling of tokenization. For this, we utilized the bert-base-uncased model and its built-in tokenizer. It is worth noting that, when calculating the BERT embeddings for single words, we focused on the word-specific token representation. This strategy differs from using the [CLS] token, which represents the entire sentence’s embedding. The reason behind this decision was to ensure a more granular representation of individual words. In contrast, the [CLS] token, although it represents the overall semantic content of the sentence 16 , does not provide a focused representation of each unique word within the sentence. We then averaged the embeddings associated with each word across different sentences to obtain a final word-level BERT embedding. This averaging was motivated from the fact that sentence-dependent word-level BERT embeddings are more similar among them compared to embeddings of different words. This is illustrated in Fig.  3 a for 10 sampled words from the dictionary. For these words, we calculated the cosine similarity between pairs of vectors reflecting context-sensitive word embeddings. This resulted in a similarity matrix, which is visualized as a heatmap, where brighter squares indicate higher similarity and darker squares indicate lower similarity. It can be observed that sentence-dependent BERT embeddings exhibit contextual variations, but are still more similar among them than to the other words. Thus, averaging across sentences allowed us to obtain contextually robust word-level embedding for each word and reduce the BERT dictionary to the same dictionary we used for Word2Vec.

Unlike BERT, CLAP is fine-tuned to reduce dissimilarity between audio and text pairs in a multimodal setting. As part of this process, CLAP aligns audio and text representations to occupy a joint multimodal space 17 . This alignment ensures that similar audio and text pairs are closer together, while dissimilar pairs are farther apart. During the fine-tuning process, the model weights, including those used to generate embeddings, are updated to minimize the loss on the specific task, which is a symmetric cross-entropy loss function. Furthermore, CLAP generates a single embedding per sentence or word because it is a fine-tuned version of BERT, with an additional Dense layer at the end of its architecture. As a result, CLAP embeddings exhibit less variation compared to BERT embeddings. This can be observed in Fig.  3 b. Given these reasons, there is no need to compute multiple-word embeddings per word across different sentences. Therefore, we constructed a dictionary where each word contained in the Super Hard Drive Combo’s labels is associated with its word embedding generated by the CLAP-text encoder.

BERT and CLAP embeddings have a dimensionality of 768 and 1024, respectively, whereas Word2vec embeddings have 300 dimensions. Thus, as a final step for calculating, we reduced the dimensionality of BERT and CLAP embeddings using an autoencoder (see supplementary material ). This reduction brought the embeddings to the same dimensionality as the Word2Vec model (300), while resulting in a negligible information loss (autoencoder reconstruction loss was \(0.89\%\) for BERT and \(0.005\%\) for CLAP,see suppl. material). We then substitute the original length embeddings of the preliminary BERT and CLAP dictionaries with theses reduced ones.

Thus, by calculating word-level embeddings and aligning the dimensions of all the models, we ensured a fair and meaningful comparison across the different semantic models. All the dictionaries have the same 9960 entities, extracted from the SoundIdeas sound labels, and an associated word representative 300-dimensional embedding. Sound-level embeddings are then obtained as the average of all word-level embeddings in the sound description.

BERT and CLAP normalized similarity matrices. Comparing BERT and CLAP embeddings across 100 different sentences reveals interesting patterns. While BERT embeddings exhibit noticeable variation for each word, implying some degree of divergence, CLAP embeddings display, in most cases, remarkable consistency and reduced variation.

Network architecture

We developed two different neural network configurations for sound recognition task: semDNN and catDNN (Fig.  2 c). Both networks resemble the VGGish 3 architecture and share similar components, such as four main convolutional blocks with 64, 128, 256, and 512 filters. Compared to VGGish 3 , we added a dropout layer (rate = 0.2 22 ) and a batch normalization layer 23 after each down-sampling operation, and after the fully connected layers to improve the model’s generalization ability, prevent overfitting, and facilitate more stable and efficient training in comparison to VGGish. We also applied global average pooling after the last convolutional block to summarize the feature maps into a fixed-length vector. However, they differ in the output layer. Whereas VGGish has a 128-unit dense layer, SemDNN has a 300-unit layer with linear activation, and catDNN has a 9960-unit dense layer with a sigmoid activation function. We used a different loss function for each of these architectures. For semDNN, we used an angular distance loss function, due to the nature of the regression task that aims to minimize the angle between the true word embedding and the word embedding predicted during training. This loss function is suitable for semantic embeddings, as it encourages the network to learn the continuous representation of words within the fitting domain 15 , 24 , 25 , 26 . On the other hand, catDNN uses a binary cross-entropy loss function, which is suitable for the multi-label classification task 27 . This loss function measures the difference between the predicted probabilities and the true labels and encourages the network to learn a discrete representation of words that can be used for classification.

SemDNN and its variant We employed different strategies to train SemDNN. Specifically, we trained SemDNN using the Word2Vec, BERT, and CLAP representations as labels. Furthermore, as a purely acoustic approach, we trained a Convolutional Auto Encoder (CAE) with the architecture depicted in Fig.  2 c for the encoder, and a reversed architecture for the decoder. The CAE was trained using only acoustic inputs, without involving a categorical/semantic label. The Mean Square Error was employed as the loss function for the CAE. To provide an additional control network, we also considered SemDNN with random Normal-HE initialization 28 , without training it. A summary of the variants is depicted in Table  1 . Additionally, to assess the efficacy of semantically balanced training, we trained SemDNN using a randomly chosen dataset of the same length as the training set that was generated from hierarchical clustering (see section “ Training dataset ”).

Preprocessing and input features The input audio clips were preprocessed as follows: first, we resampled signals to a standard 16 kHz sampling rate format and converted them to mono. Then, we split the clips into non-overlapping segments of 960 ms. For each segment, we computed a short-time Fourier transform on 25 ms Hanning-windowed frames with a step size of 10 ms. This allowed us to break down the signal into its constituent frequencies at each moment in time and perform a detailed analysis of the audio data. Next, we aggregated the resulting power spectrogram into 64 mel bands covering the range of 125–7500 Hz.

Finally, we generated a stabilized spectrogram consisting of 96-time windows per 64 log mel bins. To obtain the log mel spectrogram, we took the logarithm of the mel spectrogram values. Additionally, we applied a stabilization technique to prevent numerical instability during this step. The stabilization involved adding a small offset of 0.01 to the mel-spectrum before taking the logarithm. This offset ensures that the logarithm operation does not encounter zero values, which could lead to undefined or erroneous results. The resulting stabilized spectrogram was then utilized as the input for training and evaluation of the deep neural networks (DNNs) and it is the same procedure applied in 3 . Each 1 s sound frame inherited the same label.

Training dataset

The networks have been trained using sounds and labels from SuperHard Drive Combo (SHDC) by Sound Ideas 18 , a collection of 388,199 variable-length sounds (2584 h) covering a wide range of sound sources and events. SHDC contains 7 different natural sound databases that can be considered as independent datasets: DigiEffects 29 , General Hard Drive Combo 30 , Hollywood Edge 31 , Mike McDonough Speciality 32 , Serafine 33 , SoundStorm 34 , and Ultimate 35 .

We employed a natural language processing (NLP) pipeline to extract a dictionary of sound-descriptive words from the SHDC metadata. The initial step involved eliminating all non-informative tokens from the filename metadata. This included numbers, serial IDs, stop-words, and all non-English words that were not included in the GoogleNews300D-Word2Vec 36 model dictionary. In the majority of cases, these filenames contained information about the sound sources and events occurring in the sounds. For instance, “ ManSneezesWhugeLoCRT 026004. wav ”was reduced to “man _ sneezes” , and “ \(Rhythmic-Percussion-Variation-Short-Version-21PET10-088.wav\) ”was transformed into “rhytmic _ percussion _ variation” . Our next step was to replace nouns that were either too specific (subordinate categories) or too general (super-ordinate categories) with basic-level descriptors. For example, in first case, specific car models like “Subaru Impreza” or “Audi TT” were replaced with the more general term “car ” , and specific dog breeds like “labrador ” or “pincher” were replaced with “dog ” . In the rare case of super-ordinate categories, expressions that were excessively vague such as “animals” were replaced with basic-level descriptors that provided more specific information. For instance, for the file that was called “AnimalVarious _ DIGIMEGADISC-60.wav” , we replaced “animal _ various” , after listening to the sound with “lion _ growls _ bats _ swarm” , thus preserving the semantic integrity of the sound while avoiding excessive generality. This process was initially automated using the NLP pipeline. To ensure accuracy, the results were manually reviewed and corrected as necessary, as demonstrated in the example mentioned above. The decision to standardize descriptors to a basic level was driven by the need to balance specificity and generality, maintaining meaningful semantic information without overloading the model with excessive detail. This approach allows for a more manageable and semantically consistent representation of heterogeneous natural sounds, enhancing the model’s ability to learn and generalize from the sound-descriptive words in the SHDC metadata 37 . The resulting output of the aforementioned NLP pipeline was sound labels extracted from the filenames of the sounds and an entities-dictionary of 9960 units. The first two columns of Table  2 show some examples, note in the last row how the transformation in the base-level category occurs, from Harrier to jet .

An initial analysis of the word-frequency distribution in the database (Fig.  4 ) revealed that sound labels were highly skewed towards words such as “car”, “door”, “metal”, and “engine” from a prominent portion of the database dedicated to vehicle sounds. To rectify this imbalance, we implemented a semantics balancing procedure relying on a hierarchical clustering analysis of the Word2Vec embeddings of the sound-descriptors dictionary. We initially computed the Word2Vec embedding of each word and generated a normalized pairwise cosine similarity matrix. This matrix was subsequently input to a hierarchical clustering algorithm (ward-linkage 38 ). Different cluster counts (100, 200, 300, 400, 500) were tested to assess the impact of clustering granularity on the performance, which was measured using the evaluation procedure described in the section “ Semantic-learning accuracy ”. Our results indicated that the optimal performance was obtained with 300 clusters (see Fig.  S1 in the Supplementary Material ). Finally, we randomly selected up to 20 words from each cluster, matching the average number of words per cluster. We also chose 300 sounds for each of the selected words, leading to a more balanced dataset. The resulting balanced dataset included 273,940 sounds (training set = \(90\%\) = 246,546 sounds; 1,366,848 frames; validation set \(5\%\) ; internal evaluation set = \(5\%\) ).

Labels distributions. The most frequent word in the dataset is “car”, followed by “door” and “metal”. To avoid over-representation of the most frequent classes, we developed and applied a method for creating a semantically-balanced dataset.

In the next phase, we conducted a quality check to evaluate the spatial arrangement of the words within clusters in the embedding space. Specifically, we ranked the clusters based on their inter-cluster cosine similarity, from highest to lowest. Utilizing t-Stochastic Nearest Embedding (tSNE) 21 , we visualized the top 25 clusters, ensuring the words within each cluster were semantically related (see Fig.  5 ). The figure results in a visual representation of the semantic space. Each point in the plot corresponds to a semantically related word contained in a specific color-coded cluster. The top 25 clusters are highlighted, showcasing the arrangement of words in the reduced-dimensional space.

Evaluation datasets

We evaluated the performance of our proposed approach with four publicly-available natural sound datasets: FSD50k 10 , consisting of 10,231 44.1 kHz mono audio files and 200 labels; Environmental Sound Classification-50 (ESC-50) 39 , made up of 2000 5-s, 44.1 kHz mono, sounds and 50 label-classes; Urban Sound 8K 40 , comprising 8732 sounds with lengths of up to 4 s, 44.1 kHz mono, and 10 class labels; and Making Sense Of Sounds 41 , which includes 500 5-s, 44.1 kHz mono, sounds divided into two level categories, 5 macro-classes, and 91 subclasses. In addition, we used a \(5\%\) subset of the SoundIdeas dataset consisting of 13,697 sounds (not used for training our models) to evaluate the performance of our models (internal evaluation).

t-SNE visualization of the top 25 semantically related word clusters. Spatial arrangement of words in the embedding space, where each point represents a semantically related word. Color-coded clusters highlight the organization of words in the reduced-dimensional space, providing insight into the ideal relationships between spectrograms and their associated semantic representations.

Semantic-learning accuracy

We compared semDNN and catDNN using two prediction-accuracy metrics: Ranking score and Average Max Cosine Similarity (AMCSS). For the different variants of semDNN, which produce semantic embeddings as predictions, we employed Non-Negative Least Squares (NNLS) regression 42 to convert the embeddings back into word predictions. The models’ training involved generating embeddings using Word2Vec, BERT, and CLAP for individual labels in the sound descriptions. However, in the evaluation phase (see  section “ Semantic-learning accuracy ”) these embeddings were averaged to create one single representative semantic embedding for each sound. To retrieve the constituent single-word embeddings from the predicted mixture, we used a NNLS 42 approach. The NNLS regression projected the predicted semantic embeddings onto the single-word embedding space, considering the entire dictionary as the design matrix of dimension 9960x300. By applying a non-negativity constraint in the NNLS, the coefficients of the linear combination remained non-negative, preserving the original averaging process.

Ranking Score To evaluate the prediction accuracy of the NNLS regression coefficients, known as \(\beta\) -values (for semDNN, in all its variants) and the sigmoid output probabilities (for catDNN), we employed a ranking-based metric called the “ranking score.” This metric allows us to compare the models’ predictive abilities while considering the relative positions of the true labels within the sorted predictions. First, we obtained the NNLS \(\beta\) -values, which represent the coefficients assigned to the different words of the dictionary (9960 \(\beta\) -values). The obtained \(\beta\) -values were sorted in descending order based on their magnitudes.

Similarly, we obtained sigmoid output probabilities from CatDNN and we sorted with the same criteria. These probabilities represent the model’s confidence scores for each possible class or label. To calculate the ranking score, we utilized the sorted predictions. The ranking score is defined as follows:

Here, m represents the ranking score, N represents the length of the dictionary, and \(\text {{rank}}\) is the position in the dictionary of the predicted label corresponding to the true label. We computed the ranking score individually for each word in multi-word labels and then averaged the scores.

The ranking score penalizes predictions that deviate significantly from the true labels, resulting in a lower score for predictions ranked further away from the true label. Conversely, a higher score indicates a closer alignment between the predicted label and the ground truth. The ranking score is threshold-independent, allowing a comprehensive comparison of all words in the dictionary (9960 words) with their respective true labels.

Average Maximum Cosine Similarity Score (AMCSS) To compare the performance of the different networks we used a novel metric, the Average Maximum Cosine Similarity Score (AMCSS). The AMCSS (Average Maximum Cosine Similarity Score) is computed by considering the predicted labels and true labels. The true labels are extracted from a fixed dictionary, which is described in section “ Label encoding strategies ”. The AMCSS is defined as:

where \(\mid S \mid\) represents the number of true sound labels, S represents the set of true sound labels, P represents the set of predicted labels, \(P_{\text {N}}\) represents the top \(N = 10\) words obtained from the predicted labels, X ( s ) represents the word embeddings of the true sound label s , Y ( p ) represents the word embeddings of the predicted label p and the operation in fraction calculates the cosine similarity between the word embeddings X ( s ) and Y ( p ).

To calculate the AMCSS, we compare the word embeddings of the true labels and the top 10 words obtained from the NNLS (for Word2Vec, BERT, and CLAP) or sigmoid output (for catDNN) generated from the model predictions. We calculate the cosine similarity between each word embedding in the true labels and the top 10 words. The maximum cosine similarity value among all the comparisons is taken as the AMCSS. The AMCSS is computed as the average of the maximum cosine similarity scores for each word, ensuring more robustness as compared to using only the top word from NNLS (or sigmoid). The AMCSS reflects the network’s ability to identify relevant words and concepts associated with the true label, even if the exact label is not among the top predictions.

However, such a metric is influenced by the geometry of the manifold where the embeddings lie, and it is therefore misleading to directly compare the AMCSS obtained with Word2Vec with the one obtained, for instance, with BERT. We illustrate this problem more in detail in Fig.  6 , where we considered 500 randomly chosen sounds from the internal test set and computed the cosine similarity matrix between the predicted and true embeddings of all the sounds. The upper row represents the values on the main diagonal of the similarity matrix, i.e. the cosine similarity between the sound embedding and its prediction, for each sound. The lower row displays instead the values outside the main diagonal, thus the cosine similarities between the prediction of a sound embedding, and the true embeddings of different sounds. A model that discriminates well a correct predictions would result in high values for the diagonal elements (top row), and lower values for the off-diagonal elements (lower row). Notably, BERT and CLAP exhibit high cosine similarity values both on the diagonal and off-diagonal, resulting in a right-skewed and lower variance distribution. In addition, the difference between the mean values of the diagonal and off-diagonal is considerably smaller for BERT and CLAP. On the other hand, Word2Vec, despite having lower overall similarity values, demonstrates higher selectivity, showing greater differences between the mean values of diagonal and off-diagonal elements. Based on these findings, we computed AMCSS using the same dictionary for all language models and decided to use Word2Vec as a reference, ensuring a more discriminative metric to compare models.

Distribution of cosine similarities between true and predicted embeddings for different SemDNN variants. Cosine Similarity (CS) distributions between true embeddings and predictions of 500 random sounds from the SoundIdeas 43 test set. The upper row represents the cosine similarity between the sound embedding and its prediction, measuring the network’s accuracy in predicting embeddings for the same sound (diagonal values of the similarity matrix). The lower row shows the cosine similarities between the prediction of a sound embedding, and the true embeddings of different sounds (off-diagonal values of the similarity matrix), reflecting the network’s performance in comparing a reference sound to different sounds.

Behaviour prediction accuracy

We evaluated to what extent layer-by-layer embeddings of semDNN and its variants and catDNN, and of several control networks, including the CAE, predicted perceived dissimilarity judgments obtained with humans.

Behavioural data

In Giordano et al.’s study (Experiment 2 14 ), data were collected from two groups, each with 20 participants. Random assignment placed participants in either the sound dissimilarity or word dissimilarity condition. In the sound dissimilarity condition, participants estimated the dissimilarity between 80 natural sounds. In the word dissimilarity condition, participants assessed the dissimilarity of sentences describing the source of each sound (e.g., “meowing cat”). For the behavioural datasets, name plus verb sound descriptors were derived from the results of a preliminary verbal identification experiment ( 14 , Experiment 1), during which 20 individuals, who did not take part in Experiment 2, were asked to identify the sound-generating events using one verb and one or two nouns. In particular, for each of the sound stimuli, the name plus verb sound descriptors considered for the analyses in this study, and evaluated by participants in the word condition, were the modal verbs and nouns (that is, the most frequent verbs and nouns) across the 20 participants in the verbal identification experiment. Each condition involved evaluating two sets of 40 stimuli categorized as living or non-living objects. The stimuli had a median duration of 5.1 s. Sessions were conducted separately for each stimulus set, with the presentation order balanced across participants. Participants performed a hierarchical sorting task. Initially, they grouped similar sounds or verbal descriptors into 15 groups using onscreen icons. Clicking on the icons activated the corresponding stimuli. Participants then iteratively merged the two most similar groups until all stimuli were consolidated into one group. The dissimilarity between stimuli was determined based on the merging step at which they were grouped, with dissimilar sounds or words being merged at a later stage of the procedure than similar sounds or words. The resulting output is a dissimilarity matrix.

Cross-validated representation similarity analysis

We employed a cross-validated computational modeling framework, similar to Giordano et al. 13 , to predict behavioural dissimilarities using model distances derived from the network representations. To this purpose, we initially computed cosine the distance between stimuli within each layer of a specific network (encoder-only for CAE). For each network separately, we then used layer-specific distances to predict group-averaged behavioural dissimilarities within a cross-validated linear regression framework. More specifically, we adopted a repeated 10-fold cross-validation split-half approach to estimate the behaviour variance ( \(R^{2}_{CV}\) ) predicted by each network (100 random splits of participants into training and test groups, with independent standardization of group-averaged training and test dissimilarities). \(R^{2}_{CV}\) was estimated as \(1 - \frac{SSE_{\text {{test}}}}{SST_{\text {{test}}}}\) , where \(SSE_{\text {{test}}}\) is the sum of squared prediction errors for the test set, and \(SST_{\text {{test}}}\) is the total sum of squares for the test set. We performed 10,000 row per column permutations for each split, ensuring that the same object permutations were maintained across the splits. We also estimated the noise ceiling, representing the maximum predictable variance, to determine the need for model or data improvements 13 . This approach provided a robust framework to validate the predictive performance of our computational model against behavioural data. As additional comparison models, we considered four NLP embeddings (Word2Vec 15 , BERT 16 and CLAP 17 with no dimensionality reduction applied) to compare semantic learning in our audio-based semDNN with text-based learning. For Word2Vec, we computed a single semantic embedding for each sound by taking the average of the semantic embeddings for the name and verb sound descriptors. However, for BERT and CLAP, we directly obtained a single semantic embedding for each sound by estimating the semantic embedding for the name plus verb sentence. We also considered four pre-published categorical sound-to-event CNNs (Yamnet 3 , VGGish 3 , Kell 44 , and CNN-14 from PANNs models 45 ) along with variants of the semDNN network ( SemDNN \({\text{ BERT }}\) ,SemDNN \({\text{ CLAP }}\) ,SemDNN \({\text{ Unbal }}\) , SemDNN \({\text{ NoTrain }}\) ).

In this section, we present the results of our experiments evaluating the performance of models in predicting semantic relations between sounds and matching human behaviour in an auditory cognitive task.

The semantic predictivity of the networks was evaluated considering both the internal SuperHardDrive Combo dataset (used for training the networks), and also considering the external datasets (FSD50k, US8k, ESC-50, and MSOS) which were not used for the training or potential subsequent fine-tuning. Figure  7 shows the pairwise comparisons of the averaged Ranking Score across all evaluation sounds from the internal and external datasets for the four tested models: CatDNN, SemDNN with Word2Vec, SemDNN with CLAP, and the model trained with BERT. Additionally, Fig.  8 showcases the AMCSS comparison between the models. The graph depicts the average AMCSS or Ranking score on the two axes, with the intersection representing the corresponding metrics for each model. In the graph, points below the line of equality indicate that the model on the x-axis performs better on that dataset, and vice versa. SemDNN trained with Word2Vec emerges as having the best performance across all the comparisons, outperforming competing models in terms of both AMCSS and Ranking scores (see Fig.  9 , for bar plots of average performance metrics across all datasets).

Pairwise comparison of ranking scores among models . Averaged ranking scores of CatDNN, SemDNN with Word2Vec, SemDNN with CLAP, and SemDNN with BERT embeddings. Points below the equality line indicate better performance of the model on the x-axis for the corresponding dataset, and vice versa.

Pairwise comparison of AMCSS among models . Average AMCSS (Average Maximum Cosine Similarity Score) for CatDNN, SemDNN with Word2Vec, SemDNN with CLAP, and SemDNN with BERT embeddings. Points below the equality line indicate superior performance of the model on the x-axis for the corresponding dataset, and vice versa.

Bar plot summary comparison of Ranking Scores and AMCSS. The bar plot on the left represents the average Ranking Scores for CatDNN, SemDNN with Word2Vec, SemDNN with CLAP, and the BERT-trained model. The bar plot on the right represents the average AMCSS for the same models. SemDNN trained with Word2Vec consistently outperforms the other models.

Table  2 shows some examples of the top 5 predicted words retrieved from the NNLS, for SemDNN and its variants, and from the sigmoid activations, for catDNN. To show the results of our NLP pipeline (see section “ Training dataset ”) in order to get labels from the SoundIdeas 43 dataset, we present in the first column the name of the filenames. Note in the last row how we moved from Harrier , which is a type of fighter jet, to its base-level category.

Our hypothesis was that SemDNN embeddings would outperform CatDNN in predicting higher-order semantic relations between sounds. To test this, we evaluated the MSOS dataset (see " Evaluation datasets "), where sounds are grouped into five macro-classes: sound effects, human, music, nature, and urban. For both SemDNN and CatDNN models, we computed pairwise normalized cosine distances between sound embeddings in the last intermediate layer (Fig.  2 , arrow). In Fig.  10 , the upper left panel illustrates an idealization, which synthetically reflects the original macro-class organization constructed in the left panel. In this synthetic construction, we assigned a within-category distance of 0 and a between-category distance of 1, allowing for a clear distinction between categories.

To compare the performance of different SemDNN variants and CatDNN, we calculated normalized cosine distances and Pearson correlation coefficients between the synthetic matrix and the computed dissimilarities. The matrix in the upper right panel represents the SemDNN trained with Word2Vec embeddings dissimilarities, while the matrix in the lower left panel represents the CatDNN embeddings. The left two panels are the dissimilarities of SemDNN trained with BERT and CLAP, respectively. The color scale in all the matrices represents the normalized distances, ranging from minimum (blue) to maximum (yellow).

Among the SemDNN variants, the SemDNN model trained with Word2Vec embeddings demonstrated a stronger correlation with the true categorical model, with a Pearson correlation coefficient of 0.340. Comparatively, the SemDNN models trained with BERT and CLAP embeddings exhibited lower correlation coefficients of 0.194 and 0.224, respectively. The CatDNN embedding displayed a correlation coefficient of 0.202. Therefore, the SemDNN trained with BERT representations exhibited the weakest correlation with the true categorical model.

Comparison of embedding dissimilarity matrices for SemDNNs and CatDNN. Normalized Cosine distances between MSOS sound embeddings of the intermediate layer 512-D of CatDNN and SemDNN trained with Word2Vec, BERT, and CLAP representations (arrow in Fig.  2 ). The matrix on the upper left reflects the true macro-classes, where the color scale represents the minimum (blue) and maximum (yellow) normalized distances, specifically within-category distance = 0; between-category distance = 1. The SemDNN trained with Word2Vec embedding matrix demonstrates a stronger reflection of the macro-class organization compared to the CatDNN embedding matrix and the other two SemDNN variants, as indicated by higher Pearson correlation coefficients with the true categorical model: 0.340 for SemDNN with Word2Vec, 0.194 for SemDNN with BERT, 0.224 for SemDNN with CLAP and 0.193 for CatDNN.

Figure  11 shows the ability of the various models to predict behavioural, sound, and word, dissimilarities using the cross-validated R-squared statistic. Models are grouped in four classes: semantics (blue), acoustics(light blue), CatDNNs(green), and SemDNNs (red). The noise ceiling represents the upper bound or best possible performance given data limitations or experimental constraints.

Cross-validated RSA results for sound condition and word condition. The color distributions correspond to the plug-in distribution of \(R^{2}_{CV}\) across CV folds, represented by the box plots. The center of the box plot represents the median, while the lower and upper box limits indicate the 1st and 3rd quartiles, respectively. The bottom and top whiskers depict the data within 1.5 interquartile ranges from the 1st and 3rd quartiles, respectively. The dark gray color represents the cross-CV fold median of the permutation results. The orange color indicates the noise ceiling, with the dashed line representing the median noise ceiling across CV folds. The upper graph shows the performance of the evaluated models in predicting perceived sound dissimilarity (SemDNN outperforms all other models). The lower graph shows the performance of the evaluated models in perceived word dissimilarity, notably, Word2Vec outperforms all the other models.

Table  3 presents a summary of the two RSA results, highlighting, on the left column, SemDNN’s superior predictivity of perceived sound dissimilarity (highest \(R^{2}_{CV}\) value) than CatDNN and other models (see also Fig.  11 ). Importantly, SemDNN outperformed all the competing networks trained with categorical labels (VGGish, PANNs CNN-14, Yamnet, and Kell). CLAP was instead the most predictive of the semantic models. These results confirm our hypothesis that a network that learns continuous semantic representations from acoustics better approximates human behaviour compared to models considering only acoustic information, relying solely on semantic information, or learning categorical semantic representations from acoustics. SemDNN trained with Word2Vec representations outperformed SemDNN trained with CLAP or BERT representations. Additionally, training SemDNN on a semantically balanced dataset yielded better results compared to training on a randomly chosen dataset (semDNN \(_{\text{ unbal }}\) ), highlighting the importance of a balanced dataset 37 . We also evaluated the performance of an untrained network (semDNN \(_{\text{ notrain }}\) ) initialized with random values, serving as a baseline. The right column of Table  3 focuses on the prediction of word perceived dissimilarity of the considered models measured by their respective \(R^{2}_{CV}\) values. Notably, Word2Vec emerges as the most successful semantic model in this task. This outcome is in line with our expectations, considering the nature of our labels, which are keywords, and not actual sentences, representing the semantic content of the sounds. Among the DNN models, SemDNN stands out as the top performer in predicting word-perceived dissimilarity. This can be attributed to the fact that SemDNN is trained using Word2Vec representations, which aligns well with our keyword-based labels. The inherent strength of Word2Vec in capturing semantic relationships and similarities enables SemDNN to leverage this knowledge effectively, resulting in superior performance compared to other DNN models.

We conducted a systematic exploration of the impact of employing continuous semantic embeddings ( Word2Vec, BERT, and CLAP ) in training DNNs for sound recognition, contrasting them with categorical labels ( one-hot encoding ).

Through our experiments and analyses, we gained significant perspectives into how the choice of semantic representations influences the performance of artificial hearing algorithms.

We compared the different models and the categorical model by using averaged Ranking Scores and AMCSSs (Figs.  7 and  8 ) on various datasets (FSD50k, US8k, ESC-50, MSOS, and the internal SuperHardDrive Combo dataset). The results consistently demonstrated that SemDNN trained with Word2Vec outperformed CatDNN and SemDNNs trained with CLAP and BERT. These findings imply that training DNNs to map sounds into a dense space preserving semantic relationships between sound sources enhances the network’s ability to recognize and comprehend individual sound events. The superiority of semDNN trained with Word2Vec over those using BERT and CLAP suggests that the complexity of the optimal semantic space lies between a categorical representation, lacking semantic relations, and a context-dependent natural language space, which may involve excessively fine-grained information. Our study employed keyword labels instead of full sentences, potentially limiting models’ contextual learning. While Word2Vec performs well with keywords, BERT and CLAP are both optimized for sentence-level context and might have faced limitations in this keyword-based setup. Moreover it is worth to clarify, as that we are not using context vectors (CLS) for CLAP or BERT (see section “ Label encoding strategies ”). Instead, we are averaging the word embeddings of each word in the label, which dilutes the contextual information. These factors may have limited the effectiveness of BERT and CLAP in our current evaluation framework. It may be interesting, in future work, to conduct similar analyses with sounds described with fully-formed sentences, such as those used in automated-captioning challenges 46 , and especially focusing on words for which the BERT/CLAP embeddings show sufficient variability (see e.g. “rock” in Fig.  3 ). Nonetheless, Word2Vec outperformed these models, suggesting that natural sound semantics may not require complex contextual information for comprehension. This finding challenges the traditional view of natural sound perception’s semantic complexity, often examined through the lens of language semantics 47 . It suggests that the inherent characteristics of natural sounds, well-captured by Word2Vec’s relatively simple semantic mapping, may not necessitate the contextual information demanded by language semantics.

Our hypothesis was that DNNs that are trained to recognize sounds and simultaneously learn the semantic relation between the sources would mimic human behaviour better than other existing networks. We assessed this hypothesis in two steps: First, we examined the ability of the DNNs to form higher-order semantic classes; second, we assessed their ability to approximate human behaviour in auditory cognitive tasks.

In the first step, we focused on the MSOS dataset, which organizes sounds into five macro-classes (effects, human, music, nature, and urban). We computed the pairwise cosine distances between sound embeddings in the last intermediate layer of SemDNN and CatDNN (Fig.  10 ). The results indicated that the SemDNN embedding better reflected the macro-class organization compared to the CatDNN embedding. The Pearson correlation coefficient with the true categorical model of the five macro-classes was higher for SemDNN (0.330) compared to CatDNN (0.202). Notably, semDNN (and the other networks) were not explicitly trained to group individual sounds into macro-classes. The superiority of semDNN over catDNN underscores that semDNNs leverage the semantic relation between sound sources in addition to the acoustic similarity of specific sounds.

To address the second question, we conducted a cross-validated RSA (Representational Similarity Analysis) and assessed the performance of different models in explaining perceived sound and word dissimilarity ratings (Fig.  11 ). The results, summarized in Table  3 , demonstrated that for the sound condition, SemDNN achieved the highest \(R^{2}_{CV}\) value among all the models, indicating its superior ability to emulate human behavioural data. It outperformed not only CatDNN and other DNNs trained with categorical labels but also purely semantic models such as Word2Vec, BERT, and CLAP. Interestingly, SemDNN trained with Word2Vec representations exhibited better performance than SemDNN trained with CLAP or BERT representations. Moreover, we trained SemDNN with Word2Vec on a random choice dataset (SemDNN \({\text{ unbal }}\) ) to compare the performance with a semantically balanced dataset. The results showed that SemDNN still outperformed SemDNN \({\text{ unbal }}\) , highlighting the importance of a semantically balanced dataset in training the model. Additionally, we included a baseline model (SemDNN \(_{\text{ notrain }}\) ) that was untrained and solely initialized with random weights. For the word condition, the results are summarised in Table  3 and depicted in the lower graph of Fig.  11 . Word2Vec outperforms other semantic models in predicting word dissimilarity, as expected. Since our labels are keywords rather than complete sentences, Word2Vec effectively captures the semantic content of the sounds. None of the CatDNNs stand out among the others showing the limitations of this network to perform a simple linguistic task. On the other hand, Word2Vec’s ability to capture semantic relationships and similarities enables SemDNN to leverage this knowledge effectively, leading to superior performance compared to other DNN models. Yet, for SemDNN (and the other DNNs), the behavioural data are predicted using all layers and thus DNN-based predictions include contributions from both early-mid layers (acoustics) and late layers (semantics), which may explain the lower performance of SemDNN in the word task compared to the language models.

Overall, these results provide strong evidence for the effectiveness of SemDNN in capturing both acoustic and semantic information and approximate human behaviour in auditory cognitive tasks. Integrating acoustic and semantic features proved more successful than considering acoustic information alone (CatDNN) or relying solely on pure semantic models. It’s worth noting that SemDNNs were trained with over 1 million examples, while 1 billion examples were considered to train VGGish and Yamnet 6 , supporting the idea that—when the goal is to approximate human behaviour—ecological, balanced datasets may be more relevant than large amounts of unbalanced training data 37 .

Conclusions

In this study, we investigated the performance of various models in performing sound recognition tasks and in their ability to approximate human behaviour in auditory cognitive tasks (sound dissimilarity ratings). Our findings provide an important understanding of the role of semantic information in these two aspects. The key conclusions drawn from our analysis are as follows:

SemDNN, combining both acoustic and semantic information,consistently outperformed CatDNNs in both sound recognition performance and approximating human behavioural data. This suggests that our approach of mapping sounds to a continuous space is a valid and advantageous alternative to the conventional method of training sound-to-event DNNs for discrete sound categories.

SemDNN models trained with Word2Vec representations exhibited superior performance compared to other semantic representations like BERT or CLAP. This underscores the effectiveness of Word2Vec embeddings in basic sound recognition tasks. Future work should explore the generalizability of these findings, especially when using datasets with complex linguistic descriptions of sounds.

Training SemDNN models on a semantically balanced dataset improved the prediction of human behavioural data compared to training on a randomly chosen dataset. It outperformed many other models trained on a larger number of sounds, emphasizing the importance of dataset curation and the use of ecologically valid datasets, particularly when aiming to approximate human behaviour.

In summary, our study advances our understanding of the interplay between acoustics and semantics in both sound-to-event DNNs and human listeners. This paves the way for future research to optimize models and enhance their alignment with human perceptual judgments.

Data availability

The data that support the findings of this study are available from SoundIdeas Inc. 43 but restrictions apply to the availability of these data, which were used under Royalty free license 48 for the current study, and so are not publicly available.

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Acknowledgements

This work was supported by the Dutch Research Council (NWO 406.20.GO.030 to EF), the French National Research Agency (ANR-21-CE37-0027-01 to BLG; ANR-16-CONV-0002—ILCB; ANR11-LABX-0036—BLRI), Data Science Research Infrastructure (DSRI; Maastricht University) and the Dutch Province of Limburg.

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Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands

Michele Esposito, Giancarlo Valente & Elia Formisano

Institut des Neurosciences de La Timone, CNRS UMR 7289-Université Aix-Marseille, Marseille, France

Bruno L. Giordano

BISS Institute, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands

Yenisel Plasencia-Calaña & Elia Formisano

Institute of Data Science, Maastricht University, Maastricht, The Netherlands

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Contributions

M.E. and E.F. conceptualized the study. M.E., B.L.G, Y.P.C., E.F. developed the methodology. M.E., B.L.G. and E.F. wrote the software. M.E., G.V. and E.F. validated the results. M.E., B.L.G. and E.F. conducted the formal analysis and investigation. M.E. B.L.G. and E.F. provided resources. M.E. curated the data. M.E. and E.F. wrote the original draft of the manuscript. M.E. B.L.G. and E.F. wrote, and all authors reviewed and edited the manuscript. B.L.G. and E.F. visualized, supervised and administered the project and acquired funding.

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Correspondence to Michele Esposito or Elia Formisano .

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Esposito, M., Valente, G., Plasencia-Calaña, Y. et al. Bridging auditory perception and natural language processing with semantically informed deep neural networks. Sci Rep 14 , 20994 (2024). https://doi.org/10.1038/s41598-024-71693-9

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Received : 21 December 2023

Accepted : 30 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1038/s41598-024-71693-9

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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